BLUEPRINT FOR AN

AI BILL OF

RIGHTS

MAKING AUTOMATED

SYSTEMS WORK FOR

THE AMERICAN PEOPLE

OCTOBER 2022

About this Document

The Blueprint for an AI Bill of Rights: Making Automated Systems Work for the American People was

published by the White House Office of Science and Technology Policy in October 2022. This framework was

released one year after OSTP announced

the launch of a process to develop “a bill of rights for an AI-powered

world.” Its release follows a year of public engagement to inform this initiative. The framework is available

online at: https://www.whitehouse.gov/ostp/ai-bill-of-rights

About the Office of Science and Technology Policy

The Office of Science and Technology Policy (OSTP) was established by the National Science and Technology

Policy, Organization, and Priorities Act of 1976 to provide the President and others within the Executive Office

of the President with advice on the scientific, engineering, and technological aspects of the economy, national

security, health, foreign relations, the environment, and the technological recovery and use of resources, among

other topics. OSTP leads interagency science and technology policy coordination efforts, assists the Office of

Management and Budget (OMB) with an annual review and analysis of Federal research and development in

budgets, and serves as a source of scientific and technological analysis and judgment for the President with

respect to major policies, plans, and programs of the Federal Government.

Legal Disclaimer

The Blueprint for an AI Bill of Rights: Making Automated Systems Work for the American People is a white paper

published by the White House Office of Science and Technology Policy. It is intended to support the

development of policies and practices that protect civil rights and promote democratic values in the building,

deployment, and governance of automated systems.

The Blueprint for an AI Bill of Rights is non-binding and does not constitute U.S. government policy. It

does not supersede, modify, or direct an interpretation of any existing statute, regulation, policy, or

international instrument. It does not constitute binding guidance for the public or Federal agencies and

therefore does not require compliance with the principles described herein. It also is not determinative of what

the U.S. government’s position will be in any international negotiation. Adoption of these principles may not

meet the requirements of existing statutes, regulations, policies, or international instruments, or the

requirements of the Federal agencies that enforce them. These principles are not intended to, and do not,

prohibit or limit any lawful activity of a government agency, including law enforcement, national security, or

intelligence activities.

The appropriate application of the principles set forth in this white paper depends significantly on the

context in which automated systems are being utilized. In some circumstances, application of these principles

in whole or in part may not be appropriate given the intended use of automated systems to achieve government

agency missions. Future sector-specific guidance will likely be necessary and important for guiding the use of

automated systems in certain settings such as AI systems used as part of school building security or automated

health diagnostic systems.

The Blueprint for an AI Bill of Rights recognizes that law enforcement activities require a balancing of

equities, for example, between the protection of sensitive law enforcement information and the principle of

notice; as such, notice may not be appropriate, or may need to be adjusted to protect sources, methods, and

other law enforcement equities. Even in contexts where these principles may not apply in whole or in part,

federal departments and agencies remain subject to judicial, privacy, and civil liberties oversight as well as

existing policies and safeguards that govern automated systems, including, for example, Executive Order 13960,

Promoting the Use of Trustworthy Artificial Intelligence in the Federal Government (December 2020).

This white paper recognizes that national security (which includes certain law enforcement and

homeland security activities) and defense activities are of increased sensitivity and interest to our nation’s

adversaries and are often subject to special requirements, such as those governing classified information and

other protected data. Such activities require alternative, compatible safeguards through existing policies that

govern automated systems and AI, such as the Department of Defense (DOD) AI Ethical Principles and

Responsible AI Implementation Pathway and the Intelligence Community (IC) AI Ethics Principles and

Framework. The implementation of these policies to national security and defense activities can be informed by

the Blueprint for an AI Bill of Rights where feasible.

The Blueprint for an AI Bill of Rights is not intended to, and does not, create any legal right, benefit, or

defense, substantive or procedural, enforceable at law or in equity by any party against the United States, its

departments, agencies, or entities, its officers, employees, or agents, or any other person, nor does it constitute a

waiver of sovereign immunity.

This document is a work of the United States Government and is in the public domain (see 17 U.S.C. §105).

ECTION TITLE

FOREWORD

Among the great challenges posed to democracy today is the use of technology, data, and automated systems in

ways that threaten the rights of the American public. Too often, these tools are used to limit our opportunities and

prevent our access to critical resources or services. These problems are well documented. In America and around

the world, systems supposed to help with patient care have proven unsafe, ineffective, or biased. Algorithms used

in hiring and credit decisions have been found to reflect and reproduce existing unwanted inequities or embed

new harmful bias and discrimination. Unchecked social media data collection has been used to threaten people’s

opportunities, undermine their privacy, or pervasively track their activity—often without their knowledge or

consent.

These outcomes are deeply harmful—but they are not inevitable. Automated systems have brought about extraor-

dinary benefits, from technology that helps farmers grow food more efficiently and computers that predict storm

paths, to algorithms that can identify diseases in patients. These tools now drive important decisions across

sectors, while data is helping to revolutionize global industries. Fueled by the power of American innovation,

these tools hold the potential to redefine every part of our society and make life better for everyone.

This important progress must not come at the price of civil rights or democratic values, foundational American

principles that President Biden has affirmed as a cornerstone of his Administration. On his first day in office, the

President ordered the full Federal government to work to root out inequity, embed fairness in decision-

making processes, and affirmatively advance civil rights, equal opportunity, and racial justice in America.

The

President has spoken forcefully about the urgent challenges posed to democracy today and has regularly called

on people of conscience to act to preserve civil rights—including the right to privacy, which he has called “the

basis for so many more rights that we have come to take for granted that are ingrained in the fabric of this

country.”

To advance President Biden’s vision, the White House Office of Science and Technology Policy has identified

five principles that should guide the design, use, and deployment of automated systems to protect the American

public in the age of artificial intelligence. The Blueprint for an AI Bill of Rights is a guide for a society that

protects all people from these threats—and uses technologies in ways that reinforce our highest values.

Responding to the experiences of the American public, and informed by insights from researchers,

technologists, advocates, journalists, and policymakers, this framework is accompanied by a technical

companion—a handbook for anyone seeking to incorporate these protections into policy and practice, including

detailed steps toward actualizing these principles in the technological design process. These principles help

provide guidance whenever automated systems can meaningfully impact the public’s rights, opportunities,

or access to critical needs.

ABOUT THIS FRAMEWORK

The Blueprint for an AI Bill of Rights is a set of five principles and associated practices to help guide the

design, use, and deployment of automated systems to protect the rights of the American public in the age of

artificial intel-ligence. Developed through extensive consultation with the American public, these principles are

a blueprint for building and deploying automated systems that are aligned with democratic values and protect

civil rights, civil liberties, and privacy. The Blueprint for an AI Bill of Rights includes this Foreword, the five

principles, notes on Applying the The Blueprint for an AI Bill of Rights, and a T

echnical Companion that gives

concrete steps that can be taken by many kinds of organizations—from governments at all levels to companies of

all sizes—to uphold these values. Experts from across the private sector, governments, and international

consortia have published principles and frameworks to guide the responsible use of automated systems; this

framework provides a national values statement and toolkit that is sector-agnostic to inform building these

protections into policy, practice, or the technological design process. Where existing law or policy—such as

sector-specific privacy laws and oversight requirements—do not already provide guidance, the Blueprint for an

AI Bill of Rights should be used to inform policy decisions.

LISTENING TO THE AMERICAN PUBLIC

The White House Office of Science and Technology Policy has led a year-long process to seek and distill input

from people across the country—from impacted communities and industry stakeholders to technology develop-

ers and other experts across fields and sectors, as well as policymakers throughout the Federal government—on

the issue of algorithmic and data-driven harms and potential remedies. Through panel discussions, public listen-

ing sessions, meetings, a formal request for information, and input to a publicly accessible and widely-publicized

email address, people throughout the United States, public servants across Federal agencies, and members of the

international community spoke up about both the promises and potential harms of these technologies, and

played a central role in shaping the Blueprint for an AI Bill of Rights. The core messages gleaned from these

discussions include that AI has transformative potential to improve Americans’ lives, and that preventing the

harms of these technologies is both necessary and achievable. The Appendix includes a full list of public engage-

ments.

AI BILL OF RIGHTS

FFECTIVE SYSTEMS

ineffective systems. Automated systems should be

communities, stakeholders, and domain experts to identify

Systems should undergo pre-deployment testing, risk

that demonstrate they are safe and effective based on

including those beyond the intended use, and adherence to

protective measures should include the possibility of not

Automated systems should not be designed with an intent

reasonably foreseeable possibility of endangering your safety or the safety of your community. They should

stemming from unintended, yet foreseeable, uses or

SECTION TITLE

BLUEPRINT FOR AN

SAFE AND E

You should be protected from unsafe or

developed with consultation from diverse

concerns, risks, and potential impacts of the system.

identification and mitigation, and ongoing monitoring

their intended use, mitigation of unsafe outcomes

domain-specific standards. Outcomes of these

deploying the system or removing a system from use.

be designed to proactively protect you from harms

impacts of automated systems. You should be protected from inappropriate or irrelevant data use in the

design, development, and deployment of automated systems, and from the compounded harm of its reuse.

Independent evaluation and reporting that confirms that the system is safe and effective, including reporting of

steps taken to mitigate potential harms, should be performed and the results made public whenever possible.

ALGORITHMIC DISCRIMINATION PROTECTIONS

You should not face discrimination by algorithms and systems should be used and designed in

an equitable way. Algorithmic discrimination occurs when automated systems contribute to unjustified

different treatment or impacts disfavoring people based on their race, color, ethnicity, sex (including

pregnancy, childbirth, and related medical conditions, gender identity, intersex status, and sexual

orientation), religion, age, national origin, disability, veteran status, genetic information, or any other

classification protected by law. Depending on the specific circumstances, such algorithmic discrimination

may violate legal protections. Designers, developers, and deployers of automated systems should take

proactive and continuous measures to protect individuals and communities from algorithmic

discrimination and to use and design systems in an equitable way. This protection should include proactive

equity assessments as part of the system design, use of representative data and protection against proxies

for demographic features, ensuring accessibility for people with disabilities in design and development,

pre-deployment and ongoing disparity testing and mitigation, and clear organizational oversight. Independent

evaluation and plain language reporting in the form of an algorithmic impact assessment, including

disparity testing results and mitigation information, should be performed and made public whenever

possible to confirm these protections.

SECTION TITLE

DATA PRIVACY

You should be protected from abusive data practices via built-in protections and you

should have agency over how data about you is used. You should be protected from violations of

privacy through design choices that ensure such protections are included by default, including ensuring that

data collection conforms to reasonable expectations and that only data strictly necessary for the specific

context is collected. Designers, developers, and deployers of automated systems should seek your permission

and respect your decisions regarding collection, use, access, transfer, and deletion of your data in appropriate

ways and to the greatest extent possible; where not possible, alternative privacy by design safeguards should be

used. Systems should not employ user experience and design decisions that obfuscate user choice or burden

users with defaults that are privacy invasive. Consent should only be used to justify collection of data in cases

where it can be appropriately and meaningfully given. Any consent requests should be brief, be understandable

in plain language, and give you agency over data collection and the specific context of use; current hard-to-

understand notice-and-choice practices for broad uses of data should be changed. Enhanced protections and

restrictions for data and inferences related to sensitive domains, including health, work, education, criminal

justice, and finance, and for data pertaining to youth should put you first. In sensitive domains, your data and

related inferences should only be used for necessary functions, and you should be protected by ethical review

and use prohibitions. You and your communities should be free from unchecked surveillance; surveillance

technologies should be subject to heightened oversight that includes at least pre-deployment assessment of their

potential harms and scope limits to protect privacy and civil liberties. Continuous surveillance and monitoring

should not be used in education, work, housing, or in other contexts where the use of such surveillance

technologies is likely to limit rights, opportunities, or access. Whenever possible, you should have access to

reporting that confirms your data decisions have been respected and provides an assessment of the

potential impact of surveillance technologies on your rights, opportunities, or access.

NOTICE AND EXPLANATION

You should know that an automated system is being used and understand how and why it

contributes to outcomes that impact you. Designers, developers, and deployers of automated systems

should provide generally accessible plain language documentation including clear descriptions of the overall

system functioning and the role automation plays, notice that such systems are in use, the individual or organiza-

tion responsible for the system, and explanations of outcomes that are clear, timely, and accessible. Such notice

should be kept up-to-date and people impacted by the system should be notified of significant use case or key

functionality changes. You should know how and why an outcome impacting you was determined by an

automated system, including when the automated system is not the sole input determining the outcome.

Automated systems should provide explanations that are technically valid, meaningful and useful to you and to

any operators or others who need to understand the system, and calibrated to the level of risk based on the

context. Reporting that includes summary information about these automated systems in plain language and

assessments of the clarity and quality of the notice and explanations should be made public whenever possible.

SECTION TITLE

HUMAN ALTERNATIVES, CONSIDERATION, AND FALLBACK

You should be able to opt out, where appropriate, and have access to a person who can quickly

consider and remedy problems you encounter. You should be able to opt out from automated systems in

favor of a human alternative, where appropriate. Appropriateness should be determined based on reasonable

expectations in a given context and with a focus on ensuring broad accessibility and protecting the public from

especially harmful impacts. In some cases, a human or other alternative may be required by law. You should have

access to timely human consideration and remedy by a fallback and escalation process if an automated system

fails, it produces an error, or you would like to appeal or contest its impacts on you. Human consideration and

fallback should be accessible, equitable, effective, maintained, accompanied by appropriate operator training, and

should not impose an unreasonable burden on the public. Automated systems with an intended use within sensi-

tive domains, including, but not limited to, criminal justice, employment, education, and health, should additional-

ly be tailored to the purpose, provide meaningful access for oversight, include training for any people interacting

with the system, and incorporate human consideration for adverse or high-risk decisions. Reporting that includes

a description of these human governance processes and assessment of their timeliness, accessibility, outcomes,

and effectiveness should be made public whenever possible.

Definitions for key terms in The Blueprint for an AI Bill of Rights can be found in Applying the Blueprint for an AI Bill of Rights.

Accompanying analysis and tools for actualizing each principle can be found in the Technical Companion.

ECTION TITLE

Applying The Blueprint for an AI Bill of Rights

While many of the concerns addressed in this framework derive from the use of AI, the technical

capabilities and specific definitions of such systems change with the speed of innovation, and the potential

harms of their use occur even with less technologically sophisticated tools. Thus, this framework uses a two-

part test to determine what systems are in scope. This framework applies to (1) automated systems that (2)

have the potential to meaningfully impact the American public’s rights, opportunities, or access to

critical resources or services. These rights, opportunities, and access to critical resources of services should

be enjoyed equally and be fully protected, regardless of the changing role that automated systems may play in

our lives.

This framework describes protections that should be applied with respect to all automated systems that

have the potential to meaningfully impact individuals' or communities' exercise of:

RIGHTS, OPPORTUNITIES, OR ACCESS

Civil rights, civil liberties, and privacy, including freedom of speech, voting, and protections from discrimi-

nation, excessive punishment, unlawful surveillance, and violations of privacy and other freedoms in both

public and private sector contexts;

Equal opportunities, including equitable access to education, housing, credit, employment, and other

programs; or,

Access to critical resources or services, such as healthcare, financial services, safety, social services,

non-deceptive information about goods and services, and government benefits.

A list of examples of automated systems for which these principles should be considered is provided in the

Appendix. The Technical Companion, which follows, offers supportive guidance for any person or entity that

creates, deploys, or oversees automated systems.

Considered together, the five principles and associated practices of the Blueprint for an AI Bill of

Rights form an overlapping set of backstops against potential harms. This purposefully overlapping

framework, when taken as a whole, forms a blueprint to help protect the public from harm.

The measures taken to realize the vision set forward in this framework should be proportionate

with the extent and nature of the harm, or risk of harm, to people's rights, opportunities, and

access.

RELATIONSHIP TO EXISTING LAW AND POLICY

The Blueprint for an AI Bill of Rights is an exercise in envisioning a future where the American public is

protected from the potential harms, and can fully enjoy the benefits, of automated systems. It describes princi-

ples that can help ensure these protections. Some of these protections are already required by the U.S. Constitu-

tion or implemented under existing U.S. laws. For example, government surveillance, and data search and

seizure are subject to legal requirements and judicial oversight. There are Constitutional requirements for

human review of criminal investigative matters and statutory requirements for judicial review. Civil rights laws

protect the American people against discrimination.

SECTION TITLE

Applying The Blueprint for an AI Bill of Rights

RELATIONSHIP TO EXISTING LAW AND POLICY

There are regulatory safety requirements for medical devices, as well as sector-, population-, or technology-spe-

cific privacy and security protections. Ensuring some of the additional protections proposed in this framework

would require new laws to be enacted or new policies and practices to be adopted. In some cases, exceptions to

the principles described in the Blueprint for an AI Bill of Rights may be necessary to comply with existing law,

conform to the practicalities of a specific use case, or balance competing public interests. In particular, law

enforcement, and other regulatory contexts may require government actors to protect civil rights, civil liberties,

and privacy in a manner consistent with, but using alternate mechanisms to, the specific principles discussed in

this framework. The Blueprint for an AI Bill of Rights is meant to assist governments and the private sector in

moving principles into practice.

The expectations given in the Technical Companion are meant to serve as a blueprint for the development of

additional technical standards and practices that should be tailored for particular sectors and contexts. While

existing laws informed the development of the Blueprint for an AI Bill of Rights, this framework does not detail

those laws beyond providing them as examples, where appropriate, of existing protective measures. This

framework instead shares a broad, forward-leaning vision of recommended principles for automated system

development and use to inform private and public involvement with these systems where they have the poten-

tial to meaningfully impact rights, opportunities, or access. Additionally, this framework does not analyze or

take a position on legislative and regulatory proposals in municipal, state, and federal government, or those in

other countries.

We have seen modest progress in recent years, with some state and local governments responding to these prob-

lems with legislation, and some courts extending longstanding statutory protections to new and emerging tech-

nologies. There are companies working to incorporate additional protections in their design and use of auto-

mated systems, and researchers developing innovative guardrails. Advocates, researchers, and government

organizations have proposed principles for the ethical use of AI and other automated systems. These include

the Organization for Economic Co-operation and Development’s (OECD’s) 2019 Recommendation on Artificial

Intelligence, which includes principles for responsible stewardship of trustworthy AI and which the United

States adopted, and Executive Order 13960 on Promoting the Use of Trustworthy Artificial Intelligence in the

Federal Government, which sets out principles that govern the federal government’s use of AI. The Blueprint

for an AI Bill of Rights is fully consistent with these principles and with the direction in Executive Order 13985

on Advancing Racial Equity and Support for Underserved Communities Through the Federal Government.

These principles find kinship in the Fair Information Practice Principles (FIPPs), derived from the 1973 report

of an advisory committee to the U.S. Department of Health, Education, and Welfare, Records, Computers,

and the Rights of Citizens.

While there is no single, universal articulation of the FIPPs, these core

principles for managing information about individuals have been incorporated into data privacy laws and

policies across the globe.

The Blueprint for an AI Bill of Rights embraces elements of the FIPPs that are

particularly relevant to automated systems, without articulating a specific set of FIPPs or scoping

applicability or the interests served to a single particular domain, like privacy, civil rights and civil liberties,

ethics, or risk management. The Technical Companion builds on this prior work to provide practical next

steps to move these principles into practice and promote common approaches that allow technological

innovation to flourish while protecting people from harm.

Applying The Blueprint for an AI Bill of Rights

DEFINITIONS

ALGORITHMIC DISCRIMINATION: “Algorithmic discrimination” occurs when automated systems

contribute to unjustified different treatment or impacts disfavoring people based on their race, color, ethnicity,

sex (including pregnancy, childbirth, and related medical conditions, gender identity, intersex status, and sexual

orientation), religion, age, national origin, disability, veteran status, genetic information, or any other classifica-

tion protected by law. Depending on the specific circumstances, such algorithmic discrimination may violate

legal protections. Throughout this framework the term “algorithmic discrimination” takes this meaning (and

not a technical understanding of discrimination as distinguishing between items).

AUTOMATED SYSTEM: An "automated system" is any system, software, or process that uses computation as

whole or part of a system to determine outcomes, make or aid decisions, inform policy implementation, collect

data or observations, or otherwise interact with individuals and/or communities. Automated systems

include, but are not limited to, systems derived from machine learning, statistics, or other data processing

or artificial intelligence techniques, and exclude passive computing infrastructure. “Passive computing

infrastructure” is any intermediary technology that does not influence or determine the outcome of decision,

make or aid in decisions, inform policy implementation, or collect data or observations, including web

hosting, domain registration, networking, caching, data storage, or cybersecurity. Throughout this

framework, automated systems that are considered in scope are only those that have the potential to

meaningfully impact individuals’ or communi-ties’ rights, opportunities, or access.

COMMUNITIES: “Communities” include: neighborhoods; social network connections (both online and

offline); families (construed broadly); people connected by affinity, identity, or shared traits; and formal organi-

zational ties. This includes Tribes, Clans, Bands, Rancherias, Villages, and other Indigenous communities. AI

and other data-driven automated systems most directly collect data on, make inferences about, and may cause

harm to individuals. But the overall magnitude of their impacts may be most readily visible at the level of com-

munities. Accordingly, the concept of community is integral to the scope of the Blueprint for an AI Bill of Rights.

United States law and policy have long employed approaches for protecting the rights of individuals, but exist-

ing frameworks have sometimes struggled to provide protections when effects manifest most clearly at a com-

munity level. For these reasons, the Blueprint for an AI Bill of Rights asserts that the harms of automated

systems should be evaluated, protected against, and redressed at both the individual and community levels.

EQUITY: “Equity” means the consistent and systematic fair, just, and impartial treatment of all individuals.

Systemic, fair, and just treatment must take into account the status of individuals who belong to underserved

communities that have been denied such treatment, such as Black, Latino, and Indigenous and Native American

persons, Asian Americans and Pacific Islanders and other persons of color; members of religious minorities;

women, girls, and non-binary people; lesbian, gay, bisexual, transgender, queer, and intersex (LGBTQI+)

persons; older adults; persons with disabilities; persons who live in rural areas; and persons otherwise adversely

affected by persistent poverty or inequality.

RIGHTS, OPPORTUNITIES, OR ACCESS: “Rights, opportunities, or access” is used to indicate the scoping

of this framework. It describes the set of: civil rights, civil liberties, and privacy, including freedom of speech,

voting, and protections from discrimination, excessive punishment, unlawful surveillance, and violations of

privacy and other freedoms in both public and private sector contexts; equal opportunities, including equitable

access to education, housing, credit, employment, and other programs; or, access to critical resources or

services, such as healthcare, financial services, safety, social services, non-deceptive information about goods

and services, and government benefits.

Applying The Blueprint for an AI Bill of Rights

SENSITIVE DATA: Data and metadata are sensitive if they pertain to an individual in a sensitive domain

(defined below); are generated by technologies used in a sensitive domain; can be used to infer data from a

sensitive domain or sensitive data about an individual (such as disability-related data, genomic data, biometric

data, behavioral data, geolocation data, data related to interaction with the criminal justice system, relationship

history and legal status such as custody and divorce information, and home, work, or school environmental

data); or have the reasonable potential to be used in ways that are likely to expose individuals to meaningful

harm, such as a loss of privacy or financial harm due to identity theft. Data and metadata generated by or about

those who are not yet legal adults is also sensitive, even if not related to a sensitive domain. Such data includes,

but is not limited to, numerical, text, image, audio, or video data.

SENSITIVE DOMAINS: “Sensitive domains” are those in which activities being conducted can cause material

harms, including significant adverse effects on human rights such as autonomy and dignity, as well as civil liber-

ties and civil rights. Domains that have historically been singled out as deserving of enhanced data protections

or where such enhanced protections are reasonably expected by the public include, but are not limited to,

health, family planning and care, employment, education, criminal justice, and personal finance. In the context

of this framework, such domains are considered sensitive whether or not the specifics of a system context

would necessitate coverage under existing law, and domains and data that are considered sensitive are under-

stood to change over time based on societal norms and context.

SURVEILLANCE TECHNOLOGY: “Surveillance technology” refers to products or services marketed for

or that can be lawfully used to detect, monitor, intercept, collect, exploit, preserve, protect, transmit, and/or

retain data, identifying information, or communications concerning individuals or groups. This framework

limits its focus to both government and commercial use of surveillance technologies when juxtaposed with

real-time or subsequent automated analysis and when such systems have a potential for meaningful impact

on individuals’ or communities’ rights, opportunities, or access.

UNDERSERVED COMMUNITIES: The term “underserved communities” refers to communities that have

been systematically denied a full opportunity to participate in aspects of economic, social, and civic life, as

exemplified by the list in the preceding definition of “equity.”

FROM

PRINCIPLES

TO PRACTICE

A TECHINCAL COMPANION TO

THE

Blueprint for an

AI B

ILL OF RIGHTS

TABLE OF CONTENTS

ROM PRINCIPLES TO PRACTICE: A TECHNICAL COMPANION TO THE BLUEPRINT

FOR AN AI BILL OF RIGHTS

SING THIS TECHNICAL COMPANION

AFE AND EFFECTIVE SYSTEMS

ALGORITHMIC DISCRIMINATION PROTECTIONS

DATA PRIVACY

NOTICE AND EXPLANATION

HUMAN ALTERNATIVES, CONSIDERATION, AND FALLBACK

APPENDIX

EXAMPLES OF AUTOMATED SYSTEMS

ISTENING TO THE AMERICAN PEOPLE

NDNOTES

USING THIS TECHNICAL COMPANION

The Blueprint for an AI Bill of Rights is a set of five principles and associated practices to help guide the design,

use, and deployment of automated systems to protect the rights of the American public in the age of artificial

intelligence. This technical companion considers each principle in the Blueprint for an AI Bill of Rights and

provides examples and concrete steps for communities, industry, governments, and others to take in order to

build these protections into policy, practice, or the technological design process.

Taken together, the technical protections and practices laid out in the Blueprint for an AI Bill of Rights can help

guard the American public against many of the potential and actual harms identified by researchers, technolo-

gists, advocates, journalists, policymakers, and communities in the United States and around the world. This

technical companion is intended to be used as a reference by people across many circumstances – anyone

impacted by automated systems, and anyone developing, designing, deploying, evaluating, or making policy to

govern the use of an automated system.

Each principle is accompanied by three supplemental sections:

WHY THIS PRINCIPLE IS IMPORTANT:

This section provides a brief summary of the problems that the principle seeks to address and protect against, including

illustrative examples.

WHAT SHOULD BE EXPECTED OF AUTOMATED SYSTEMS:

• The expectations for automated systems are meant to serve as a blueprint for the development of additional technical

standards and practices that should be tailored for particular sectors and contexts.

• This section outlines practical steps that can be implemented to realize the vision of the Blueprint for an AI Bill of Rights. The

expectations laid out often mirror existing practices for technology development, including pre-deployment testing, ongoing

monitoring, and governance structures for automated systems, but also go further to address unmet needs for change and offer

concrete directions for how those changes can be made.

• Expectations about reporting are intended for the entity developing or using the automated system. The resulting reports can

be provided to the public, regulators, auditors, industry standards groups, or others engaged in independent review, and should

be made public as much as possible consistent with law, regulation, and policy, and noting that intellectual property, law

enforcement, or national security considerations may prevent public release. Where public reports are not possible, the

information should be provided to oversight bodies and privacy, civil liberties, or other ethics officers charged with safeguard

ing individuals’ rights. These reporting expectations are important for transparency, so the American people can have

confidence that their rights, opportunities, and access as well as their expectations about technologies are respected.

HOW THESE PRINCIPLES CAN MOVE INTO PRACTICE:

This section provides real-life examples of how these guiding principles can become reality, through laws, policies, and practices.

It describes practical technical and sociotechnical approaches to protecting rights, opportunities, and access.

The examples provided are not critiques or endorsements, but rather are offered as illustrative cases to help

provide a concrete vision for actualizing the Blueprint for an AI Bill of Rights. Effectively implementing these

processes require the cooperation of and collaboration among industry, civil society, researchers, policymakers,

technologists, and the public.

SAFE AND EFFECTIVE SYSTEMS

You should be protected from unsafe or ineffective sys-

tems. Automated systems should be developed with consultation

from diverse communities, stakeholders, and domain experts to iden-

tify concerns, risks, and potential impacts of the system. Systems

should undergo pre-deployment testing, risk identification and miti-

gation, and ongoing monitoring that demonstrate they are safe and

effective based on their intended use, mitigation of unsafe outcomes

including those beyond the intended use, and adherence to do-

main-specific standards. Outcomes of these protective measures

should include the possibility of not deploying the system or remov-

ing a system from use. Automated systems should not be designed

with an intent or reasonably foreseeable possibility of endangering

your safety or the safety of your community. They should be designed

to proactively protect you from harms stemming from unintended,

yet foreseeable, uses or impacts of automated systems. You should be

protected from inappropriate or irrelevant data use in the design, de-

velopment, and deployment of automated systems, and from the

compounded harm of its reuse. Independent evaluation and report-

ing that confirms that the system is safe and effective, including re-

porting of steps taken to mitigate potential harms, should be per-

formed and the results made public whenever possible.

SAFE AND EFFECTIVE

SYSTEMS

WHY THIS PRINCIPLE IS IMPORTANT

This section provides a brief summary of the problems which the principle seeks to address and protect

against, including illustrative examples.

While technologies are being deployed to solve problems across a wide array of issues, our reliance on technology can

also lead to its use in situations where it has not yet been proven to work—either at all or within an acceptable range

of error. In other cases, technologies do not work as intended or as promised, causing substantial and unjustified harm.

Automated systems sometimes rely on data from other systems, including historical data, allowing irrelevant informa-

tion from past decisions to infect decision-making in unrelated situations. In some cases, technologies are purposeful-

ly designed to violate the safety of others, such as technologies designed to facilitate stalking; in other cases, intended

or unintended uses lead to unintended harms.

Many of the harms resulting from these technologies are preventable, and actions are already being taken to protect

the public. Some companies have put in place safeguards that have prevented harm from occurring by ensuring that

key development decisions are vetted by an ethics review; others have identified and mitigated harms found through

pre-deployment testing and ongoing monitoring processes. Governments at all levels have existing public consulta-

tion processes that may be applied when considering the use of new automated systems, and existing product develop-

ment and testing practices already protect the American public from many potential harms.

Still, these kinds of practices are deployed too rarely and unevenly. Expanded, proactive protections could build on

these existing practices, increase confidence in the use of automated systems, and protect the American public. Inno-

vators deserve clear rules of the road that allow new ideas to flourish, and the American public deserves protections

from unsafe outcomes. All can benefit from assurances that automated systems will be designed, tested, and consis-

tently confirmed to work as intended, and that they will be proactively protected from foreseeable unintended harm-

ful outcomes.

•

A proprietary model was developed to predict the likelihood of sepsis in hospitalized patients and was imple-

mented at hundreds of hospitals around the country. An independent study showed that the model predictions

underperformed relative to the designer’s claims while also causing ‘alert fatigue’ by falsely alerting

likelihood of sepsis.

•

On social media, Black people who quote and criticize racist messages have had their own speech silenced when

a platform’s automated moderation system failed to distinguish this “counter speech” (or other critique

and journalism) from the original hateful messages to which such speech responded.

•

A device originally developed to help people track and find lost items has been used as a tool by stalkers to track

victims’ locations in violation of their privacy and safety. The device manufacturer took steps after release to

protect people from unwanted tracking by alerting people on their phones when a device is found to be moving

with them over time and also by having the device make an occasional noise, but not all phones are able

to receive the notification and the devices remain a safety concern due to their misuse.

•

An algorithm used to deploy police was found to repeatedly send police to neighborhoods they regularly visit,

even if those neighborhoods were not the ones with the highest crime rates. These incorrect crime predictions

were the result of a feedback loop generated from the reuse of data from previous arrests and algorithm

predictions.

SAFE AND EFFECTIVE

SYSTEMS

WHY THIS PRINCIPLE IS IMPORTANT

This section provides a brief summary of the problems which the principle seeks to address and protect

against, including illustrative examples.

•

AI-enabled “nudification” technology that creates images where people appear to be nude—including apps that

enable non-technical users to create or alter images of individuals without their consent—has proliferated at an

alarming rate. Such technology is becoming a common form of image-based abuse that disproportionately

impacts women. As these tools become more sophisticated, they are producing altered images that are increasing-

ly realistic and are difficult for both humans and AI to detect as inauthentic. Regardless of authenticity, the expe-

rience of harm to victims of non-consensual intimate images can be devastatingly real—affecting their personal

and professional lives, and impacting their mental and physical health.

•

A company installed AI-powered cameras in its delivery vans in order to evaluate the road safety habits of its driv-

ers, but the system incorrectly penalized drivers when other cars cut them off or when other events beyond

their control took place on the road. As a result, drivers were incorrectly ineligible to receive a bonus.

SAFE AND EFFECTIVE

SYSTEMS

WHAT SHOULD BE EXPECTED OF AUTOMATED SYSTEMS

The expectations for automated systems are meant to serve as a blueprint for the development of additional

technical standards and practices that are tailored for particular sectors and contexts.

In order to ensure that an automated system is safe and effective, it should include safeguards to protect the

public from harm in a proactive and ongoing manner; avoid use of data inappropriate for or irrelevant to the task

at hand, including reuse that could cause compounded harm; and demonstrate the safety and effectiveness of

the system. These expectations are explained below.

Protect the public from harm in a proactive and ongoing manner

Consultation. The public should be consulted in the design, implementation, deployment, acquisition, and

maintenance phases of automated system development, with emphasis on early-stage consultation before a

system is introduced or a large change implemented. This consultation should directly engage diverse impact-

ed communities to consider concerns and risks that may be unique to those communities, or disproportionate-

ly prevalent or severe for them. The extent of this engagement and the form of outreach to relevant stakehold-

ers may differ depending on the specific automated system and development phase, but should include

subject matter, sector-specific, and context-specific experts as well as experts on potential impacts such as

civil rights, civil liberties, and privacy experts. For private sector applications, consultations before product

launch may need to be confidential. Government applications, particularly law enforcement applications or

applications that raise national security considerations, may require confidential or limited engagement based

on system sensitivities and preexisting oversight laws and structures. Concerns raised in this consultation

should be documented, and the automated system developers were proposing to create, use, or deploy should

be reconsidered based on this feedback.

Testing. Systems should undergo extensive testing before deployment. This testing should follow

domain-specific best practices, when available, for ensuring the technology will work in its real-world

context. Such testing should take into account both the specific technology used and the roles of any human

operators or reviewers who impact system outcomes or effectiveness; testing should include both automated

systems testing and human-led (manual) testing. Testing conditions should mirror as closely as possible the

conditions in which the system will be deployed, and new testing may be required for each deployment to

account for material differences in conditions from one deployment to another. Following testing, system

performance should be compared with the in-place, potentially human-driven, status quo procedures, with

existing human performance considered as a performance baseline for the algorithm to meet pre-deployment,

and as a lifecycle minimum performance standard. Decision possibilities resulting from performance testing

should include the possibility of not deploying the system.

Risk identification and mitigation. Before deployment, and in a proactive and ongoing manner, poten-

tial risks of the automated system should be identified and mitigated. Identified risks should focus on the

potential for meaningful impact on people’s rights, opportunities, or access and include those to impacted

communities that may not be direct users of the automated system, risks resulting from purposeful misuse of

the system, and other concerns identified via the consultation process. Assessment and, where possible, mea-

surement of the impact of risks should be included and balanced such that high impact risks receive attention

and mitigation proportionate with those impacts. Automated systems with the intended purpose of violating

the safety of others should not be developed or used; systems with such safety violations as identified unin-

tended consequences should not be used until the risk can be mitigated. Ongoing risk mitigation may necessi-

tate rollback or significant modification to a launched automated system.

SAFE AND EFFECTIVE

SYSTEMS

WHAT SHOULD BE EXPECTED OF AUTOMATED SYSTEMS

The expectations for automated systems are meant to serve as a blueprint for the development of additional

technical standards and practices that are tailored for particular sectors and contexts.

Ongoing monitoring. Automated systems should have ongoing monitoring procedures, including recalibra-

tion procedures, in place to ensure that their performance does not fall below an acceptable level over time,

based on changing real-world conditions or deployment contexts, post-deployment modification, or unexpect-

ed conditions. This ongoing monitoring should include continuous evaluation of performance metrics and

harm assessments, updates of any systems, and retraining of any machine learning models as necessary, as well

as ensuring that fallback mechanisms are in place to allow reversion to a previously working system. Monitor-

ing should take into account the performance of both technical system components (the algorithm as well as

any hardware components, data inputs, etc.) and human operators. It should include mechanisms for testing

the actual accuracy of any predictions or recommendations generated by a system, not just a human operator’s

determination of their accuracy. Ongoing monitoring procedures should include manual, human-led monitor-

ing as a check in the event there are shortcomings in automated monitoring systems. These monitoring proce-

dures should be in place for the lifespan of the deployed automated system.

Clear organizational oversight. Entities responsible for the development or use of automated systems

should lay out clear governance structures and procedures. This includes clearly-stated governance proce-

dures before deploying the system, as well as responsibility of specific individuals or entities to oversee ongoing

assessment and mitigation. Organizational stakeholders including those with oversight of the business process

or operation being automated, as well as other organizational divisions that may be affected due to the use of

the system, should be involved in establishing governance procedures. Responsibility should rest high enough

in the organization that decisions about resources, mitigation, incident response, and potential rollback can be

made promptly, with sufficient weight given to risk mitigation objectives against competing concerns. Those

holding this responsibility should be made aware of any use cases with the potential for meaningful impact on

people’s rights, opportunities, or access as determined based on risk identification procedures. In some cases,

it may be appropriate for an independent ethics review to be conducted before deployment.

Avoid inappropriate, low-quality, or irrelevant data use and the compounded harm of its

reuse

Relevant and high-quality data. Data used as part of any automated system’s creation, evaluation, or

deployment should be relevant, of high quality, and tailored to the task at hand. Relevancy should be

established based on research-backed demonstration of the causal influence of the data to the specific use case

or justified more generally based on a reasonable expectation of usefulness in the domain and/or for the

system design or ongoing development. Relevance of data should not be established solely by appealing to

its historical connection to the outcome. High quality and tailored data should be representative of the task at

hand and errors from data entry or other sources should be measured and limited. Any data used as the target

of a prediction process should receive particular attention to the quality and validity of the predicted outcome

or label to ensure the goal of the automated system is appropriately identified and measured. Additionally,

justification should be documented for each data attribute and source to explain why it is appropriate to use

that data to inform the results of the automated system and why such use will not violate any applicable laws.

In cases of high-dimensional and/or derived attributes, such justifications can be provided as overall

descriptions of the attribute generation process and appropriateness.

SAFE AND EFFECTIVE

SYSTEMS

WHAT SHOULD BE EXPECTED OF AUTOMATED SYSTEMS

The expectations for automated systems are meant to serve as a blueprint for the development of additional

technical standards and practices that are tailored for particular sectors and contexts.

Derived data sources tracked and reviewed carefully. Data that is derived from other data through

the use of algorithms, such as data derived or inferred from prior model outputs, should be identified and

tracked, e.g., via a specialized type in a data schema. Derived data should be viewed as potentially high-risk

inputs that may lead to feedback loops, compounded harm, or inaccurate results. Such sources should be care-

fully validated against the risk of collateral consequences.

Data reuse limits in sensitive domains. Data reuse, and especially data reuse in a new context, can result

in the spreading and scaling of harms. Data from some domains, including criminal justice data and data indi-

cating adverse outcomes in domains such as finance, employment, and housing, is especially sensitive, and in

some cases its reuse is limited by law. Accordingly, such data should be subject to extra oversight to ensure

safety and efficacy. Data reuse of sensitive domain data in other contexts (e.g., criminal data reuse for civil legal

matters or private sector use) should only occur where use of such data is legally authorized and, after examina-

tion, has benefits for those impacted by the system that outweigh identified risks and, as appropriate, reason-

able measures have been implemented to mitigate the identified risks. Such data should be clearly labeled to

identify contexts for limited reuse based on sensitivity. Where possible, aggregated datasets may be useful for

replacing individual-level sensitive data.

Demonstrate the safety and effectiveness of the system

Independent evaluation. Automated systems should be designed to allow for independent evaluation (e.g.,

via application programming interfaces). Independent evaluators, such as researchers, journalists, ethics

review boards, inspectors general, and third-party auditors, should be given access to the system and samples

of associated data, in a manner consistent with privacy, security, law, or regulation (including, e.g., intellectual

property law), in order to perform such evaluations. Mechanisms should be included to ensure that system

access for evaluation is: provided in a timely manner to the deployment-ready version of the system; trusted to

provide genuine, unfiltered access to the full system; and truly independent such that evaluator access cannot

be revoked without reasonable and verified justification.

Reporting.

Entities responsible for the development or use of automated systems should provide

regularly-updated reports that include: an overview of the system, including how it is embedded in the

organization’s business processes or other activities, system goals, any human-run procedures that form a

part of the system, and specific performance expectations; a description of any data used to train machine

learning models or for other purposes, including how data sources were processed and interpreted, a

summary of what data might be missing, incomplete, or erroneous, and data relevancy justifications; the

results of public consultation such as concerns raised and any decisions made due to these concerns; risk

identification and management assessments and any steps taken to mitigate potential harms; the results of

performance testing including, but not limited to, accuracy, differential demographic impact, resulting

error rates (overall and per demographic group), and comparisons to previously deployed systems;

ongoing monitoring procedures and regular performance testing reports, including monitoring frequency,

results, and actions taken; and the procedures for and results from independent evaluations. Reporting

should be provided in a plain language and machine-readable manner.

SAFE AND EFFECTIVE

SYSTEMS

HOW THESE PRINCIPLES CAN MOVE INTO PRACTICE

Real-life examples of how these principles can become reality, through laws, policies, and practical

technical and sociotechnical approaches to protecting rights, opportunities, and access.

Executive Order 13960 on Promoting the Use of Trustworthy Artificial Intelligence in the

Federal Government requires that certain federal agencies adhere to nine principles when

designing, developing, acquiring, or using AI for purposes other than national security or

defense. These principles—while taking into account the sensitive law enforcement and other contexts in which

the federal government may use AI, as opposed to private sector use of AI—require that AI is: (a) lawful and

respectful of our Nation’s values; (b) purposeful and performance-driven; (c) accurate, reliable, and effective; (d)

safe, secure, and resilient; (e) understandable; (f ) responsible and traceable; (g) regularly monitored; (h) transpar

ent; and, (i) accountable. The Blueprint for an AI Bill of Rights is consistent with the Executive Order.

Affected agencies across the federal government have released AI use case inventories

and are implementing

plans to bring those AI systems into compliance with the Executive Order or retire them.

The law and policy landscape for motor vehicles shows that strong safety regulations—and

measures to address harms when they occur—can enhance innovation in the context of com-

plex technologies. Cars, like automated digital systems, comprise a complex collection of components.

The National Highway Traffic Safety Administration,

through its rigorous standards and independent

evaluation, helps make sure vehicles on our roads are safe without limiting manufacturers’ ability to

innovate.

At the same time, rules of the road are implemented locally to impose contextually appropriate

requirements on drivers, such as slowing down near schools or playgrounds.

From large companies to start-ups, industry is providing innovative solutions that allow

organizations to mitigate risks to the safety and efficacy of AI systems, both before

deployment and through monitoring over time.

These innovative solutions include risk

assessments, auditing mechanisms, assessment of organizational procedures, dashboards to allow for ongoing

monitoring, documentation procedures specific to model assessments, and many other strategies that aim to

mitigate risks posed by the use of AI to companies’ reputation, legal responsibilities, and other product safety

and effectiveness concerns.

The Office of Management and Budget (OMB) has called for an expansion of opportunities

for meaningful stakeholder engagement in the design of programs and services. OMB also

points to numerous examples of effective and proactive stakeholder engagement, including the Community-

Based Participatory Research Program developed by the National Institutes of Health and the participatory

technology assessments developed by the National Oceanic and Atmospheric Administration.

The National Institute of Standards and Technology (NIST) is developing a risk

management framework to better manage risks posed to individuals, organizations, and

society by AI.

The NIST AI Risk Management Framework, as mandated by Congress, is intended for

voluntary use to help incorporate trustworthiness considerations into the design, development, use, and

evaluation of AI products, services, and systems. The NIST framework is being developed through a consensus-

driven, open, transparent, and collaborative process that includes workshops and other opportunities to provide

input. The NIST framework aims to foster the development of innovative approaches to address

characteristics of trustworthiness including accuracy, explainability and interpretability, reliability, privacy,

robustness, safety, security (resilience), and mitigation of unintended and/or harmful bias, as well as of

harmful uses. The NIST framework will consider and encompass principles such as

transparency, accountability, and fairness during pre-design, design and development, deployment, use,

and testing and evaluation of AI technologies and systems. It is expected to be released in the winter of 2022-23.

SAFE AND EFFECTIVE

SYSTEMS

HOW THESE PRINCIPLES CAN MOVE INTO PRACTICE

Real-life examples of how these principles can become reality, through laws, policies, and practical

technical and sociotechnical approaches to protecting rights, opportunities, and access.

Some U.S government agencies have developed specific frameworks for ethical use of AI

systems. The Department of Energy (DOE) has activated the AI Advancement Council that oversees coordina

tion and advises on implementation of the DOE AI Strategy and addresses issues and/or escalations on the

ethical use and development of AI

systems.

The Department of Defense has adopted Artificial Intelligence

Ethical Principles, and tenets for Responsible Artificial Intelligence specifically tailored to its national

security and defense

activities.

Similarly, the U.S. Intelligence Community (IC) has developed the Principles

of Artificial Intelligence Ethics for the Intelligence Community to guide personnel on whether and how to

develop and use AI in furtherance of the IC's mission, as well as an AI Ethics Framework to help implement

these

principles.

The National Science Foundation (NSF) funds extensive research to help foster the

development of automated systems that adhere to and advance their safety, security and

effectiveness. Multiple NSF programs support research that directly addresses many of these principles:

the National AI Research Institutes

support research on all aspects of safe, trustworthy, fair, and explainable

AI algorithms and systems; the Cyber Physical Systems

program supports research on developing safe

autonomous and cyber physical systems with AI components; the Secure and Trustworthy Cyberspace

program supports research on cybersecurity and privacy enhancing technologies in automated systems; the

Formal Methods in the Field

program supports research on rigorous formal verification and analysis of

automated systems and machine learning, and the Designing Accountable Software Systems

program supports

research on rigorous and reproducible methodologies for developing software systems with legal and regulatory

compliance in mind.

Some state legislatures have placed strong transparency and validity requirements on

the use of pretrial risk assessments. The use of algorithmic pretrial risk assessments has been a

cause of concern for civil rights

groups.

Idaho Code Section 19-1910, enacted in 2019,

requires that any

pretrial risk assessment, before use in the state, first be "shown to be free of bias against any class of

individuals protected from discrimination by state or federal law", that any locality using a pretrial risk

assessment must first formally validate the claim of its being free of bias, that "all documents, records, and

information used to build or validate the risk assessment shall be open to public inspection," and that assertions

of trade secrets cannot be used "to quash discovery in a criminal matter by a party to a criminal case."

ALGORITHMIC DISCRIMINATION Protections

You should not face discrimination by algorithms

and systems should be used and designed in an

equitable way. Algorithmic discrimination occurs when

automated systems contribute to unjustified different treatment or

impacts disfavoring people based on their race, color, ethnicity,

sex (including pregnancy, childbirth, and related medical

conditions, gender identity, intersex status, and sexual

orientation), religion, age, national origin, disability, veteran status,

genetic infor-mation, or any other classification protected by law.

Depending on the specific circumstances, such algorithmic

discrimination may violate legal protections. Designers, developers,

and deployers of automated systems should take proactive and

continuous measures to protect individuals and communities

from algorithmic discrimination and to use and design systems in

an equitable way. This protection should include proactive equity

assessments as part of the system design, use of representative data

and protection against proxies for demographic features, ensuring

accessibility for people with disabilities in design and development,

pre-deployment and ongoing disparity testing and mitigation, and

clear organizational oversight. Independent evaluation and plain

language reporting in the form of an algorithmic impact assessment,

including disparity testing results and mitigation information,

should be performed and made public whenever possible to confirm

these protections.

Algorithmic

Discrimination

Protections

WHY THIS PRINCIPLE IS IMPORTANT

This section provides a brief summary of the problems which the principle seeks to address and protect

against, including illustrative examples.

There is extensive evidence showing that automated systems can produce inequitable outcomes and amplify

existing inequity.

Data that fails to account for existing systemic biases in American society can result in a range of

consequences. For example, facial recognition technology that can contribute to wrongful and discriminatory

arrests,

hiring algorithms that inform discriminatory decisions, and healthcare algorithms that discount

the severity of certain diseases in Black Americans. Instances of discriminatory practices built into and

resulting from AI and other automated systems exist across many industries, areas, and contexts. While automated

systems have the capacity to drive extraordinary advances and innovations, algorithmic discrimination

protections should be built into their design, deployment, and ongoing use.

Many companies, non-profits, and federal government agencies are already taking steps to ensure the public

is protected from algorithmic discrimination. Some companies have instituted bias testing as part of their product

quality assessment and launch procedures, and in some cases this testing has led products to be changed or not

launched, preventing harm to the public. Federal government agencies have been developing standards and guidance

for the use of automated systems in order to help prevent bias. Non-profits and companies have developed best

practices for audits and impact assessments to help identify potential algorithmic discrimination and provide

transparency to the public in the mitigation of such biases.

But there is much more work to do to protect the public from algorithmic discrimination to use and design

automated systems in an equitable way. The guardrails protecting the public from discrimination in their daily

lives should include their digital lives and impacts—basic safeguards against abuse, bias, and discrimination to

ensure that all people are treated fairly when automated systems are used. This includes all dimensions of their

lives, from hiring to loan approvals, from medical treatment and payment to encounters with the criminal

justice system. Ensuring equity should also go beyond existing guardrails to consider the holistic impact that

automated systems make on underserved communities and to institute proactive protections that support these

communities.

•

An automated system using nontraditional factors such as educational attainment and employment history as

part of its loan underwriting and pricing model was found to be much more likely to charge an applicant who

attended a Historically Black College or University (HBCU) higher loan prices for refinancing a student loan

than an applicant who did not attend an HBCU. This was found to be true even when controlling for

other credit-related factors.

•

A hiring tool that learned the features of a company's employees (predominantly men) rejected women appli-

cants for spurious and discriminatory reasons; resumes with the word “women’s,” such as “women’s

chess club captain,” were penalized in the candidate ranking.

•

A predictive model marketed as being able to predict whether students are likely to drop out of school was

used by more than 500 universities across the country. The model was found to use race directly as a predictor,

and also shown to have large disparities by race; Black students were as many as four times as likely as their

otherwise similar white peers to be deemed at high risk of dropping out. These risk scores are used by advisors

to guide students towards or away from majors, and some worry that they are being used to guide

Black students away from math and science subjects.

•

A risk assessment tool designed to predict the risk of recidivism for individuals in federal custody showed

evidence of disparity in prediction. The tool overpredicts the risk of recidivism for some groups of color on the

general recidivism tools, and underpredicts the risk of recidivism for some groups of color on some of the

violent recidivism tools. The Department of Justice is working to reduce these disparities and has

publicly released a report detailing its review of the tool.

WHY THIS PRINCIPLE IS IMPORTANT

This section provides a brief summary of the problems which the principle seeks to address and protect

against, including illustrative examples.

• An automated sentiment analyzer, a tool often used by technology platforms to determine whether a state-

ment posted online expresses a positive or negative sentiment, was found to be biased against Jews and gay

people. For example, the analyzer marked the statement “I’m a Jew” as representing a negative sentiment,

while “I’m a Christian” was identified as expressing a positive sentiment.

This could lead to the

preemptive blocking of social media comments such as: “I’m gay.” A related company with this bias concern

has made their data public to encourage researchers to help address the issue

and has released reports

identifying and measuring this problem as well as detailing attempts to address it.

• Searches for “Black girls,” “Asian girls,” or “Latina girls” return predominantly

sexualized content, rather

than role models, toys, or activities.

Some search engines have been working to reduce the prevalence of

these results, but the problem remains.

• Advertisement delivery systems that predict who is most likely to click on a job advertisement end up deliv-

ering ads in ways that reinforce racial and gender stereotypes, such as overwhelmingly directing supermar-

ket cashier ads to women and jobs with taxi companies to primarily Black people.

•

Body scanners, used by TSA at airport checkpoints, require the operator to select a “male” or “female”

scanning setting based on the passenger’s sex, but the setting is chosen based on the operator’s perception of

the passenger’s gender identity. These scanners are more likely to flag transgender travelers as requiring

extra screening done by a person. Transgender travelers have described degrading experiences associated

with these extra screenings.

TSA has recently announced plans to implement a gender-neutral algorithm

while simultaneously enhancing the security effectiveness capabilities of the existing technology.

•

The National Disabled Law Students Association expressed concerns that individuals with disabilities were

more likely to be flagged as potentially suspicious by remote proctoring AI systems because of their disabili-

ty-specific access needs such as needing longer breaks or using screen readers or dictation software.

•

An algorithm designed to identify patients with high needs for healthcare systematically assigned lower

scores (indicating that they were not as high need) to Black patients than to those of white patients, even

when those patients had similar numbers of chronic conditions and other markers of health.

In addition,

healthcare clinical algorithms that are used by physicians to guide clinical decisions may include

sociodemographic variables that adjust or “correct” the algorithm’s output on the basis of a patient’s race or

ethnicity, which can lead to race-based health inequities.

Algorithmic

Discrimination

Protections

WHAT SHOULD BE EXPECTED OF AUTOMATED SYSTEMS

The expectations for automated systems are meant to serve as a blueprint for the development of additional

technical standards and practices that are tailored for particular sectors and contexts.

Any automated system should be tested to help ensure it is free from algorithmic discrimination before it can be

sold or used. Protection against algorithmic discrimination should include designing to ensure equity, broadly

construed. Some algorithmic discrimination is already prohibited under existing anti-discrimination law. The

expectations set out below describe proactive technical and policy steps that can be taken to not only

reinforce those legal protections but extend beyond them to ensure equity for underserved communities

even in circumstances where a specific legal protection may not be clearly established. These protections

should be instituted throughout the design, development, and deployment process and are described below

roughly in the order in which they would be instituted.

Protect the public from algorithmic discrimination in a proactive and ongoing manner

Proactive assessment of equity in design. Those responsible for the development, use, or oversight of

automated systems should conduct proactive equity assessments in the design phase of the technology

research and development or during its acquisition to review potential input data, associated historical

context, accessibility for people with disabilities, and societal goals to identify potential discrimination and

effects on equity resulting from the introduction of the technology. The assessed groups should be as inclusive

as possible of the underserved communities mentioned in the equity definition: Black, Latino, and Indigenous

and Native American persons, Asian Americans and Pacific Islanders and other persons of color; members of

religious minorities; women, girls, and non-binary people; lesbian, gay, bisexual, transgender, queer, and inter-

sex (LGBTQI+) persons; older adults; persons with disabilities; persons who live in rural areas; and persons

otherwise adversely affected by persistent poverty or inequality. Assessment could include both qualitative

and quantitative evaluations of the system. This equity assessment should also be considered a core part of the

goals of the consultation conducted as part of the safety and efficacy review.

Representative and robust data. Any data used as part of system development or assessment should be

representative of local communities based on the planned deployment setting and should be reviewed for bias

based on the historical and societal context of the data. Such data should be sufficiently robust to identify and

help to mitigate biases and potential harms.

Guarding against proxies. Directly using demographic information in the design, development, or

deployment of an automated system (for purposes other than evaluating a system for discrimination or using

a system to counter discrimination) runs a high risk of leading to algorithmic discrimination and should be

avoided. In many cases, attributes that are highly correlated with demographic features, known as proxies, can

contribute to algorithmic discrimination. In cases where use of the demographic features themselves would

lead to illegal algorithmic discrimination, reliance on such proxies in decision-making (such as that facilitated

by an algorithm) may also be prohibited by law. Proactive testing should be performed to identify proxies by

testing for correlation between demographic information and attributes in any data used as part of system

design, development, or use. If a proxy is identified, designers, developers, and deployers should remove the

proxy; if needed, it may be possible to identify alternative attributes that can be used instead. At a minimum,

organizations should ensure a proxy feature is not given undue weight and should monitor the system closely

for any resulting algorithmic discrimination.

Algorithmic

Discrimination

Protections

WHAT SHOULD BE EXPECTED OF AUTOMATED SYSTEMS

The expectations for automated systems are meant to serve as a blueprint for the development of additional

technical standards and practices that are tailored for particular sectors and contexts.

Ensuring accessibility during design, development, and deployment. Systems should be

designed, developed, and deployed by organizations in ways that ensure accessibility to people with disabili

ties. This should include consideration of a wide variety of disabilities, adherence to relevant accessibility

standards, and user experience research both before and after deployment to identify and address any accessi

bility barriers to the use or effectiveness of the automated system.

Disparity assessment. Automated systems should be tested using a broad set of measures to assess wheth-

er the system components, both in pre-deployment testing and in-context deployment, produce disparities.

The demographics of the assessed groups should be as inclusive as possible of race, color, ethnicity, sex

(including pregnancy, childbirth, and related medical conditions, gender identity, intersex status, and sexual

orientation), religion, age, national origin, disability, veteran status, genetic information, or any other classifi

cation protected by law. The broad set of measures assessed should include demographic performance mea-

sures, overall and subgroup parity assessment, and calibration. Demographic data collected for disparity

assessment should be separated from data used for the automated system and privacy protections should be

instituted; in some cases it may make sense to perform such assessment using a data sample. For every

instance where the deployed automated system leads to different treatment or impacts disfavoring the identi

fied groups, the entity governing, implementing, or using the system should document the disparity and a

justification for any continued use of the system.

Disparity mitigation. When a disparity assessment identifies a disparity against an assessed group, it may

be appropriate to take steps to mitigate or eliminate the disparity. In some cases, mitigation or elimination of

the disparity may be required by law. Disparities that have the potential to lead to algorithmic

discrimination, cause meaningful harm, or violate equity

goals should be mitigated. When designing and

evaluating an automated system, steps should be taken to evaluate multiple models and select the one that

has the least adverse impact, modify data input choices, or otherwise identify a system with fewer

disparities. If adequate mitigation of the disparity is not possible, then the use of the automated system

should be reconsidered. One of the considerations in whether to use the system should be the validity of any

target measure; unobservable targets may result in the inappropriate use of proxies. Meeting these

standards may require instituting mitigation procedures and other protective measures to address

algorithmic discrimination, avoid meaningful harm, and achieve equity goals.

Ongoing monitoring and mitigation. Automated systems should be regularly monitored to assess algo-

rithmic discrimination that might arise from unforeseen interactions of the system with inequities not

accounted for during the pre-deployment testing, changes to the system after deployment, or changes to the

context of use or associated data. Monitoring and disparity assessment should be performed by the entity

deploying or using the automated system to examine whether the system has led to algorithmic discrimina

tion when deployed. This assessment should be performed regularly and whenever a pattern of unusual

results is occurring. It can be performed using a variety of approaches, taking into account whether and how

demographic information of impacted people is available, for example via testing with a sample of users or via

qualitative user experience research. Riskier and higher-impact systems should be monitored and assessed

more frequently. Outcomes of this assessment should include additional disparity mitigation, if needed, or

fallback to earlier procedures in the case that equity standards are no longer met and can't be mitigated, and

prior mechanisms provide better adherence to equity standards.

Algorithmic

Discrimination

Protections

WHAT SHOULD BE EXPECTED OF AUTOMATED SYSTEMS

The expectations for automated systems are meant to serve as a blueprint for the development of additional

technical standards and practices that are tailored for particular sectors and contexts.

Demonstrate that the system protects against algorithmic discrimination

Independent evaluation. As described in the section on Safe and Effective Systems, entities should allow

independent evaluation of potential algorithmic discrimination caused by automated systems they use or

oversee. In the case of public sector uses, these independent evaluations should be made public unless law

enforcement or national security restrictions prevent doing so. Care should be taken to balance individual

privacy with evaluation data access needs; in many cases, policy-based and/or technological innovations and

controls allow access to such data without compromising privacy.

Reporting. Entities responsible for the development or use of automated systems should provide

reporting of an appropriately designed algorithmic impact assessment,

with clear specification of who

performs the assessment, who evaluates the system, and how corrective actions are taken (if necessary) in

response to the assessment. This algorithmic impact assessment should include at least: the results of any

consultation, design stage equity assessments (potentially including qualitative analysis), accessibility

designs and testing, disparity testing, document any remaining disparities, and detail any mitigation

implementation and assessments. This algorithmic impact assessment should be made public whenever

possible. Reporting should be provided in a clear and machine-readable manner using plain language to

allow for more straightforward public accountability.

Algorithmic

Discrimination

Protections

HOW THESE PRINCIPLES CAN MOVE INTO PRACTICE

Real-life examples of how these principles can become reality, through laws, policies, and practical

technical and sociotechnical approaches to protecting rights, opportunities, and access.

The federal government is working to combat discrimination in mortgage lending. The Depart-

ment of Justice has launched a nationwide initiative to combat redlining, which includes reviewing how

lenders who may be avoiding serving communities of color are conducting targeted marketing and advertising.

This initiative will draw upon strong partnerships across federal agencies, including the Consumer Financial

Protection Bureau and prudential regulators. The Action Plan to Advance Property Appraisal and Valuation

Equity includes a commitment from the agencies that oversee mortgage lending to include a

nondiscrimination standard in the proposed rules for Automated Valuation Models.

The Equal Employment Opportunity Commission and the Department of Justice have clearly

laid out how employers’ use of AI and other automated systems can result in

discrimination against job applicants and employees with disabilities.

The documents explain

how employers’ use of software that relies on algorithmic decision-making may violate existing requirements

under Title I of the Americans with Disabilities Act (“ADA”). This technical assistance also provides practical

tips to employers on how to comply with the ADA, and to job applicants and employees who think that their

rights may have been violated.

Disparity assessments identified harms to Black patients' healthcare access. A widely

used healthcare algorithm relied on the cost of each patient’s past medical care to predict future medical needs,

recommending early interventions for the patients deemed most at risk. This process discriminated

against Black patients, who generally have less access to medical care and therefore have generated less cost

than white patients with similar illness and need. A landmark study documented this pattern and proposed

practical ways that were shown to reduce this bias, such as focusing specifically on active chronic health

conditions or avoidable future costs related to emergency visits and hospitalization.

Large employers have developed best practices to scrutinize the data and models used

for hiring. An industry initiative has developed Algorithmic Bias Safeguards for the Workforce, a structured

questionnaire that businesses can use proactively when procuring software to evaluate workers. It covers

specific technical questions such as the training data used, model training process, biases identified, and

mitigation steps employed.

Standards organizations have developed guidelines to incorporate accessibility criteria

into technology design processes. The most prevalent in the United States is the Access Board’s Section

508 regulations,

which are the technical standards for federal information communication technology (software,

hardware, and web). Other standards include those issued by the International Organization for

Standardization,

and the World Wide Web Consortium Web Content Accessibility Guidelines,

a globally

recognized voluntary consensus standard for web content and other information and communications

technology.

NIST has released Special Publication 1270, Towards a Standard for Identifying and Managing Bias

in Artificial Intelligence.

The special publication: describes the stakes and challenges of bias in artificial

intelligence and provides examples of how and why it can chip away at public trust; identifies three categories

of bias in AI – systemic, statistical, and human – and describes how and where they contribute to harms; and

describes three broad challenges for mitigating bias – datasets, testing and evaluation, and human factors – and

introduces preliminary guidance for addressing them. Throughout, the special publication takes a socio-

technical perspective to identifying and managing AI bias.

Algorithmic

Discrimination

Protections

You should be protected from abusive data practices via built-in

protections and you should have agency over how data about

you is used. You should be protected from violations of privacy through

design choices that ensure such protections are included by default, including

ensuring that data collection conforms to reasonable expectations and that

only data strictly necessary for the specific context is collected. Designers, de-

velopers, and deployers of automated systems should seek your permission

and respect your decisions regarding collection, use, access, transfer, and de-

letion of your data in appropriate ways and to the greatest extent possible;

where not possible, alternative privacy by design safeguards should be used.

Systems should not employ user experience and design decisions that obfus-

cate user choice or burden users with defaults that are privacy invasive. Con-

sent should only be used to justify collection of data in cases where it can be

appropriately and meaningfully given. Any consent requests should be brief,

be understandable in plain language, and give you agency over data collection

and the specific context of use; current hard-to-understand no-

tice-and-choice practices for broad uses of data should be changed. Enhanced

protections and restrictions for data and inferences related to sensitive do-

mains, including health, work, education, criminal justice, and finance, and

for data pertaining to youth should put you first. In sensitive domains, your

data and related inferences should only be used for necessary functions, and

you should be protected by ethical review and use prohibitions. You and your

communities should be free from unchecked surveillance; surveillance tech-

nologies should be subject to heightened oversight that includes at least

pre-deployment assessment of their potential harms and scope limits to pro-

tect privacy and civil liberties. Continuous surveillance and monitoring

should not be used in education, work, housing, or in other contexts where the

use of such surveillance technologies is likely to limit rights, opportunities, or

access. Whenever possible, you should have access to reporting that confirms

your data decisions have been respected and provides an assessment of the

potential impact of surveillance technologies on your rights, opportunities, or

access.

DATA PRIVACY

WHY THIS PRINCIPLE IS IMPORTANT

This section provides a brief summary of the problems which the principle seeks to address and protect

against, including illustrative examples.

Data privacy is a foundational and cross-cutting principle required for achieving all others in this framework. Surveil-

lance and data collection, sharing, use, and reuse now sit at the foundation of business models across many industries,

with more and more companies tracking the behavior of the American public, building individual profiles based on

this data, and using this granular-level information as input into automated systems that further track, profile, and

impact the American public. Government agencies, particularly law enforcement agencies, also use and help develop

a variety of technologies that enhance and expand surveillance capabilities, which similarly collect data used as input

into other automated systems that directly impact people’s lives. Federal law has not grown to address the expanding

scale of private data collection, or of the ability of governments at all levels to access that data and leverage the means

of private collection.

Meanwhile, members of the American public are often unable to access their personal data or make critical decisions

about its collection and use. Data brokers frequently collect consumer data from numerous sources without

consumers’ permission or knowledge.

Moreover, there is a risk that inaccurate and faulty data can be used to

make decisions about their lives, such as whether they will qualify for a loan or get a job. Use of surveillance

technologies has increased in schools and workplaces, and, when coupled with consequential management and

evaluation decisions, it is leading to mental health harms such as lowered self-confidence, anxiety, depression, and

a reduced ability to use analytical reasoning.

Documented patterns show that personal data is being aggregated by

data brokers to profile communities in harmful ways.

The impact of all this data harvesting is corrosive,

breeding distrust, anxiety, and other mental health problems; chilling speech, protest, and worker organizing; and

threatening our democratic process.

The American public should be protected from these growing risks.

Increasingly, some companies are taking these concerns seriously and integrating mechanisms to protect consumer

privacy into their products by design and by default, including by minimizing the data they collect, communicating

collection and use clearly, and improving security practices. Federal government surveillance and other collection and

use of data is governed by legal protections that help to protect civil liberties and provide for limits on data retention

in some cases. Many states have also enacted consumer data privacy protection regimes to address some of these

harms.

However, these are not yet standard practices, and the United States lacks a comprehensive statutory or regulatory

framework governing the rights of the public when it comes to personal data. While a patchwork of laws exists to

guide the collection and use of personal data in specific contexts, including health, employment, education, and credit,

it can be unclear how these laws apply in other contexts and in an increasingly automated society. Additional protec-

tions would assure the American public that the automated systems they use are not monitoring their activities,

collecting information on their lives, or otherwise surveilling them without context-specific consent or legal authori-

ty.

DATA PRIVACY

WHY THIS PRINCIPLE IS IMPORTANT

This section provides a brief summary of the problems which the principle seeks to address and protect

against, including illustrative examples.

•

An insurer might collect data from a person's social media presence as part of deciding what life

insurance rates they should be offered.

•

A data broker harvested large amounts of personal data and then suffered a breach, exposing hundreds of

thousands of people to potential identity theft.

•

A local public housing authority installed a facial recognition system at the entrance to housing complexes to

assist law enforcement with identifying individuals viewed via camera when police reports are filed, leading

the community, both those living in the housing complex and not, to have videos of them sent to the local

police department and made available for scanning by its facial recognition software.

•

Companies use surveillance software to track employee discussions about union activity and use the

resulting data to surveil individual employees and surreptitiously intervene in discussions.

DATA PRIVACY

WHAT SHOULD BE EXPECTED OF AUTOMATED SYSTEMS

The expectations for automated systems are meant to serve as a blueprint for the development of additional

technical standards and practices that are tailored for particular sectors and contexts.

Traditional terms of service—the block of text that the public is accustomed to clicking through when using a web-

site or digital app—are not an adequate mechanism for protecting privacy. The American public should be protect-

ed via built-in privacy protections, data minimization, use and collection limitations, and transparency, in addition

to being entitled to clear mechanisms to control access to and use of their data—including their metadata—in a

proactive, informed, and ongoing way. Any automated system collecting, using, sharing, or storing personal data

should meet these expectations.

Protect privacy by design and by default

Privacy by design and by default. Automated systems should be designed and built with privacy protect-

ed by default. Privacy risks should be assessed throughout the development life cycle, including privacy risks

from reidentification, and appropriate technical and policy mitigation measures should be implemented. This

includes potential harms to those who are not users of the automated system, but who may be harmed by

inferred data, purposeful privacy violations, or community surveillance or other community harms. Data

collection should be minimized and clearly communicated to the people whose data is collected. Data should

only be collected or used for the purposes of training or testing machine learning models if such collection and

use is legal and consistent with the expectations of the people whose data is collected. User experience

research should be conducted to confirm that people understand what data is being collected about them and

how it will be used, and that this collection matches their expectations and desires.

Data collection and use-case scope limits. Data collection should be limited in scope, with specific,

narrow identified goals, to avoid "mission creep." Anticipated data collection should be determined to be

strictly necessary to the identified goals and should be minimized as much as possible. Data collected based on

these identified goals and for a specific context should not be used in a different context without assessing for

new privacy risks and implementing appropriate mitigation measures, which may include express consent.

Clear timelines for data retention should be established, with data deleted as soon as possible in accordance

with legal or policy-based limitations. Determined data retention timelines should be documented and justi-

fied.

Risk identification and mitigation. Entities that collect, use, share, or store sensitive data should

attempt to proactively identify harms and seek to manage them so as to avoid, mitigate, and respond appropri-

ately to identified risks. Appropriate responses include determining not to process data when the privacy risks

outweigh the benefits or implementing measures to mitigate acceptable risks. Appropriate responses do not

include sharing or transferring the privacy risks to users via notice or consent requests where users could not

reasonably be expected to understand the risks without further support.

Privacy-preserving security. Entities creating, using, or governing automated systems should follow

privacy and security best practices designed to ensure data and metadata do not leak beyond the specific

consented use case. Best practices could include using privacy-enhancing cryptography or other types of

privacy-enhancing technologies or fine-grained permissions and access control mechanisms, along with

conventional system security protocols.

DATA PRIVACY

WHAT SHOULD BE EXPECTED OF AUTOMATED SYSTEMS

The expectations for automated systems are meant to serve as a blueprint for the development of additional

technical standards and practices that are tailored for particular sectors and contexts.

Protect the public from unchecked surveillance

Heightened oversight of surveillance. Surveillance or monitoring systems should be subject to

heightened oversight that includes at a minimum assessment of potential harms during design (before deploy-

ment) and in an ongoing manner, to ensure that the American public’s rights, opportunities, and access are

protected. This assessment should be done before deployment and should give special attention to ensure

there is not algorithmic discrimination, especially based on community membership, when deployed in a

specific real-world context. Such assessment should then be reaffirmed in an ongoing manner as long as the

system is in use.

Limited and proportionate surveillance. Surveillance should be avoided unless it is strictly necessary

to achieve a legitimate purpose and it is proportionate to the need. Designers, developers, and deployers of

surveillance systems should use the least invasive means of monitoring available and restrict monitoring to the

minimum number of subjects possible. To the greatest extent possible consistent with law enforcement and

national security needs, individuals subject to monitoring should be provided with clear and specific notice

before it occurs and be informed about how the data gathered through surveillance will be used.

Scope limits on surveillance to protect rights and democratic values. Civil liberties and civil

rights must not be limited by the threat of surveillance or harassment facilitated or aided by an automated

system. Surveillance systems should not be used to monitor the exercise of democratic rights, such as voting,

privacy, peaceful assembly, speech, or association, in a way that limits the exercise of civil rights or civil liber-

ties. Information about or algorithmically-determined assumptions related to identity should be carefully

limited if used to target or guide surveillance systems in order to avoid algorithmic discrimination; such iden-

tity-related information includes group characteristics or affiliations, geographic designations, location-based

and association-based inferences, social networks, and biometrics. Continuous surveillance and monitoring

systems should not be used in physical or digital workplaces (regardless of employment status), public educa-

tional institutions, and public accommodations. Continuous surveillance and monitoring systems should not

be used in a way that has the effect of limiting access to critical resources or services or suppressing the exer-

cise of rights, even where the organization is not under a particular duty to protect those rights.

Provide the public with mechanisms for appropriate and meaningful consent, access, and

control over their data

Use-specific consent. Consent practices should not allow for abusive surveillance practices. Where data

collectors or automated systems seek consent, they should seek it for specific, narrow use contexts, for specif-

ic time durations, and for use by specific entities. Consent should not extend if any of these conditions change;

consent should be re-acquired before using data if the use case changes, a time limit elapses, or data is trans-

ferred to another entity (including being shared or sold). Consent requested should be limited in scope and

should not request consent beyond what is required. Refusal to provide consent should be allowed, without

adverse effects, to the greatest extent possible based on the needs of the use case.

Brief and direct consent requests. When seeking consent from users short, plain language consent

requests should be used so that users understand for what use contexts, time span, and entities they are

providing data and metadata consent. User experience research should be performed to ensure these consent

requests meet performance standards for readability and comprehension. This includes ensuring that consent

requests are accessible to users with disabilities and are available in the language(s) and reading level appro-

priate for the audience. User experience design choices that intentionally obfuscate or manipulate user

choice (i.e., “dark patterns”) should be not be used.

DATA PRIVACY

WHAT SHOULD BE EXPECTED OF AUTOMATED SYSTEMS

The expectations for automated systems are meant to serve as a blueprint for the development of additional

technical standards and practices that are tailored for particular sectors and contexts.

Data access and correction. People whose data is collected, used, shared, or stored by automated

systems should be able to access data and metadata about themselves, know who has access to this data, and

be able to correct it if necessary. Entities should receive consent before sharing data with other entities and

should keep records of what data is shared and with whom.

Consent withdrawal and data deletion. Entities should allow (to the extent legally permissible) with-

drawal of data access consent, resulting in the deletion of user data, metadata, and the timely removal of

their data from any systems (e.g., machine learning models) derived from that data.

Automated system support. Entities designing, developing, and deploying automated systems should

establish and maintain the capabilities that will allow individuals to use their own automated systems to help

them make consent, access, and control decisions in a complex data ecosystem. Capabilities include machine

readable data, standardized data formats, metadata or tags for expressing data processing permissions and

preferences and data provenance and lineage, context of use and access-specific tags, and training models for

assessing privacy risk.

Demonstrate that data privacy and user control are protected

Independent evaluation. As described in the section on Safe and Effective Systems, entities should allow

independent evaluation of the claims made regarding data policies. These independent evaluations should be

made public whenever possible. Care will need to be taken to balance individual privacy with evaluation data

access needs.

Reporting. When members of the public wish to know what data about them is being used in a system, the

entity responsible for the development of the system should respond quickly with a report on the data it has

collected or stored about them. Such a report should be machine-readable, understandable by most users, and

include, to the greatest extent allowable under law, any data and metadata about them or collected from them,

when and how their data and metadata were collected, the specific ways that data or metadata are being used,

who has access to their data and metadata, and what time limitations apply to these data. In cases where a user

user privacy. Additionally, summary reporting should be proactively made public with general information

about how peoples’ data and metadata is used, accessed, and stored. Summary reporting should include the

results of any surveillance pre-deployment assessment, including disparity assessment in the real-world

deployment context, the specific identified goals of any data collection, and the assessment done to ensure

only the minimum required data is collected. It should also include documentation about the scope limit

assessments, including data retention timelines and associated justification, and an assessment of the

impact of surveillance or data collection on rights, opportunities, and access. Where possible, this

assessment of the impact of surveillance should be done by an independent party. Reporting should be

provided in a clear and machine-readable manner.

DATA PRIVACY

EXTRA PROTECTIONS FOR DATA RELATED TO SENSITIVE

DOMAINS

Some domains, including health, employment, education, criminal justice, and personal finance, have long been

singled out as sensitive domains deserving of enhanced data protections. This is due to the intimate nature of these

domains as well as the inability of individuals to opt out of these domains in any meaningful way, and the

historical discrimination that has often accompanied data knowledge.

Domains understood by the public to be

sensitive also change over time, including because of technological developments. Tracking and monitoring

technologies, personal tracking devices, and our extensive data footprints are used and misused more than ever

before; as such, the protections afforded by current legal guidelines may be inadequate. The American public

deserves assurances that data related to such sensitive domains is protected and used appropriately and only in

narrowly defined contexts with clear benefits to the individual and/or society.

To this end, automated systems that collect, use, share, or store data related to these sensitive domains should meet

additional expectations. Data and metadata are sensitive if they pertain to an individual in a sensitive domain (defined

below); are generated by technologies used in a sensitive domain; can be used to infer data from a sensitive domain or

sensitive data about an individual (such as disability-related data, genomic data, biometric data, behavioral data,

geolocation data, data related to interaction with the criminal justice system, relationship history and legal status such

as custody and divorce information, and home, work, or school environmental data); or have the reasonable potential

to be used in ways that are likely to expose individuals to meaningful harm, such as a loss of privacy or financial harm

due to identity theft. Data and metadata generated by or about those who are not yet legal adults is also sensitive, even

if not related to a sensitive domain. Such data includes, but is not limited to, numerical, text, image, audio, or video

data. “Sensitive domains” are those in which activities being conducted can cause material harms, including signifi-

cant adverse effects on human rights such as autonomy and dignity, as well as civil liberties and civil rights. Domains

that have historically been singled out as deserving of enhanced data protections or where such enhanced protections

are reasonably expected by the public include, but are not limited to, health, family planning and care, employment,

education, criminal justice, and personal finance. In the context of this framework, such domains are considered

sensitive whether or not the specifics of a system context would necessitate coverage under existing law, and domains

and data that are considered sensitive are understood to change over time based on societal norms and context.

DATA PRIVACY

EXTRA PROTECTIONS FOR DATA RELATED TO SENSITIVE

DOMAINS

•

Continuous positive airway pressure machines gather data for medical purposes, such as diagnosing sleep

apnea, and send usage data to a patient’s insurance company, which may subsequently deny coverage for the

device based on usage data. Patients were not aware that the data would be used in this way or monitored

by anyone other than their doctor.

•

A department store company used predictive analytics applied to collected consumer data to determine that a

teenage girl was pregnant, and sent maternity clothing ads and other baby-related advertisements to her

house, revealing to her father that she was pregnant.

•

School audio surveillance systems monitor student conversations to detect potential "stress indicators" as

a warning of potential violence.

Online proctoring systems claim to detect if a student is cheating on an

exam using biometric markers.

These systems have the potential to limit student freedom to express a range

of emotions at school and may inappropriately flag students with disabilities who need accommodations or

use screen readers or dictation software as cheating.

•

Location data, acquired from a data broker, can be used to identify people who visit abortion clinics.

•

Companies collect student data such as demographic information, free or reduced lunch status, whether

they've used drugs, or whether they've expressed interest in LGBTQI+ groups, and then use that data to

forecast student success.

Parents and education experts have expressed concern about collection of such

sensitive data without express parental consent, the lack of transparency in how such data is being used, and

the potential for resulting discriminatory impacts.

•

Many employers transfer employee data to third party job verification services. This information is then used

by potential future employers, banks, or landlords. In one case, a former employee alleged that a

company supplied false data about her job title which resulted in a job offer being revoked.

DATA PRIVACY

WHAT SHOULD BE EXPECTED OF AUTOMATED SYSTEMS

The expectations for automated systems are meant to serve as a blueprint for the development of additional

technical standards and practices that are tailored for particular sectors and contexts.

In addition to the privacy expectations above for general non-sensitive data, any system collecting, using, shar-

ing, or storing sensitive data should meet the expectations below. Depending on the technological use case and

based on an ethical assessment, consent for sensitive data may need to be acquired from a guardian and/or child.

Provide enhanced protections for data related to sensitive domains

Necessary functions only. Sensitive data should only be used for functions strictly necessary for that

domain or for functions that are required for administrative reasons (e.g., school attendance records), unless

consent is acquired, if appropriate, and the additional expectations in this section are met. Consent for non-

necessary functions should be optional, i.e., should not be required, incentivized, or coerced in order to

receive opportunities or access to services. In cases where data is provided to an entity (e.g., health insurance

company) in order to facilitate payment for such a need, that data should only be used for that purpose.

Ethical review and use prohibitions. Any use of sensitive data or decision process based in part on sensi-

tive data that might limit rights, opportunities, or access, whether the decision is automated or not, should go

through a thorough ethical review and monitoring, both in advance and by periodic review (e.g., via an indepen-

dent ethics committee or similarly robust process). In some cases, this ethical review may determine that data

should not be used or shared for specific uses even with consent. Some novel uses of automated systems in this

context, where the algorithm is dynamically developing and where the science behind the use case is not well

established, may also count as human subject experimentation, and require special review under organizational

compliance bodies applying medical, scientific, and academic human subject experimentation ethics rules and

governance procedures.

Data quality. In sensitive domains, entities should be especially careful to maintain the quality of data to

avoid adverse consequences arising from decision-making based on flawed or inaccurate data. Such care is

necessary in a fragmented, complex data ecosystem and for datasets that have limited access such as for fraud

prevention and law enforcement. It should be not left solely to individuals to carry the burden of reviewing and

correcting data. Entities should conduct regular, independent audits and take prompt corrective measures to

maintain accurate, timely, and complete data.

Limit access to sensitive data and derived data. Sensitive data and derived data should not be sold,

shared, or made public as part of data brokerage or other agreements. Sensitive data includes data that can be

used to infer sensitive information; even systems that are not directly marketed as sensitive domain technologies

are expected to keep sensitive data private. Access to such data should be limited based on necessity and based

on a principle of local control, such that those individuals closest to the data subject have more access while

those who are less proximate do not (e.g., a teacher has access to their students’ daily progress data while a

superintendent does not).

Reporting. In addition to the reporting on data privacy (as listed above for non-sensitive data), entities devel-

oping technologies related to a sensitive domain and those collecting, using, storing, or sharing sensitive data

should, whenever appropriate, regularly provide public reports describing: any data security lapses or breaches

that resulted in sensitive data leaks; the number, type, and outcomes of ethical pre-reviews undertaken; a

description of any data sold, shared, or made public, and how that data was assessed to determine it did not pres-

ent a sensitive data risk; and ongoing risk identification and management procedures, and any mitigation added

based on these procedures. Reporting should be provided in a clear and machine-readable manner.

DATA PRIVACY

OW THESE PRINCIPLES CAN MOVE INTO PRACTICE

Real-life examples of how these principles can become reality, through laws, policies, and practical

technical and sociotechnical approaches to protecting rights, opportunities, and access.

The Privacy Act of 1974 requires privacy protections for personal information in federal

records systems, including limits on data retention, and also provides individuals a general

right to access and correct their data. Among other things, the Privacy Act limits the storage of individual

information in federal systems of records, illustrating the principle of limiting the scope of data retention. Under

the Privacy Act, federal agencies may only retain data about an individual that is “relevant and necessary” to

accomplish an agency’s statutory purpose or to comply with an Executive Order of the President. The law allows

for individuals to be able to access any of their individual information stored in a federal system of records, if not

included under one of the systems of records exempted pursuant to the Privacy Act. In these cases, federal agen

cies must provide a method for an individual to determine if their personal information is stored in a particular

system of records, and must provide procedures for an individual to contest the contents of a record about them.

Further, the Privacy Act allows for a cause of action for an individual to seek legal relief if a federal agency does not

comply with the Privacy Act’s requirements. Among other things, a court may order a federal agency to amend or

correct an individual’s information in its records or award monetary damages if an inaccurate, irrelevant, untimely,

or incomplete record results in an adverse determination about an individual’s “qualifications, character, rights, …

opportunities…, or benefits.”

NIST’s Privacy Framework provides a comprehensive, detailed and actionable approach for

organizations to manage privacy risks. The NIST Framework gives organizations ways to identify and

communicate their privacy risks and goals to support ethical decision-making in system, product, and service

design or deployment, as well as the measures they are taking to demonstrate compliance with applicable laws

or regulations. It has been voluntarily adopted by organizations across many different sectors around the world.

A school board’s attempt to surveil public school students—undertaken without

adequate community input—sparked a state-wide biometrics moratorium.

Reacting to a plan in

the city of Lockport, New York, the state’s legislature banned the use of facial recognition systems and other

“biometric identifying technology” in schools until July 1, 2022.

The law additionally requires that a report on

the privacy, civil rights, and civil liberties implications of the use of such technologies be issued before

biometric identification technologies can be used in New York schools.

Federal law requires employers, and any consultants they may retain, to report the costs

of surveilling employees in the context of a labor dispute, providing a transparency

mechanism to help protect worker organizing. Employers engaging in workplace surveillance "where

an object there-of, directly or indirectly, is […] to obtain information concerning the activities of employees or a

labor organization in connection with a labor dispute" must report expenditures relating to this surveillance to

the Department of Labor Office of Labor-Management Standards, and consultants who employers retain for

these purposes must also file reports regarding their activities.

Privacy choices on smartphones show that when technologies are well designed, privacy

and data agency can be meaningful and not overwhelming. These choices—such as contextual, timely

alerts about location tracking—are brief, direct, and use-specific. Many of the expectations listed here for

privacy by design and use-specific consent mirror those distributed to developers as best practices when

developing for smart phone devices,

such as being transparent about how user data will be used, asking for app

permissions during their use so that the use-context will be clear to users, and ensuring that the app will still

work if users deny (or later revoke) some permissions.

You should know that an automated system is being used,

and understand how and why it contributes to outcomes

that impact you. Designers, developers, and deployers of automat-

ed systems should provide generally accessible plain language docu-

mentation including clear descriptions of the overall system func-

tioning and the role automation plays, notice that such systems are in

use, the individual or organization responsible for the system, and ex-

planations of outcomes that are clear, timely, and accessible. Such

notice should be kept up-to-date and people impacted by the system

should be notified of significant use case or key functionality chang-

es. You should know how and why an outcome impacting you was de-

termined by an automated system, including when the automated

system is not the sole input determining the outcome. Automated

systems should provide explanations that are technically valid,

meaningful and useful to you and to any operators or others who

need to understand the system, and calibrated to the level of risk

based on the context. Reporting that includes summary information

about these automated systems in plain language and assessments of

the clarity and quality of the notice and explanations should be made

public whenever possible.

NOTICE AND EXPLANATION

NOTICE &

EXPLANATION

WHY THIS PRINCIPLE IS IMPORTANT

This section provides a brief summary of the problems which the principle seeks to address and protect

against, including illustrative examples.

Automated systems now determine opportunities, from employment to credit, and directly shape the American

public’s experiences, from the courtroom to online classrooms, in ways that profoundly impact people’s lives. But this

expansive impact is not always visible. An applicant might not know whether a person rejected their resume or a

hiring algorithm moved them to the bottom of the list. A defendant in the courtroom might not know if a judge deny-

ing their bail is informed by an automated system that labeled them “high risk.” From correcting errors to contesting

decisions, people are often denied the knowledge they need to address the impact of automated systems on their lives.

Notice and explanations also serve an important safety and efficacy purpose, allowing experts to verify the reasonable-

ness of a recommendation before enacting it.

In order to guard against potential harms, the American public needs to know if an automated system is being used.

Clear, brief, and understandable notice is a prerequisite for achieving the other protections in this framework. Like-

wise, the public is often unable to ascertain how or why an automated system has made a decision or contributed to a

particular outcome. The decision-making processes of automated systems tend to be opaque, complex, and, therefore,

unaccountable, whether by design or by omission. These factors can make explanations both more challenging and

more important, and should not be used as a pretext to avoid explaining important decisions to the people impacted

by those choices. In the context of automated systems, clear and valid explanations should be recognized as a baseline

requirement.

Providing notice has long been a standard practice, and in many cases is a legal requirement, when, for example,

making a video recording of someone (outside of a law enforcement or national security context). In some cases, such

as credit, lenders are required to provide notice and explanation to consumers. Techniques used to automate the

process of explaining such systems are under active research and improvement and such explanations can take many

forms. Innovative companies and researchers are rising to the challenge and creating and deploying explanatory

systems that can help the public better understand decisions that impact them.

While notice and explanation requirements are already in place in some sectors or situations, the American public

deserve to know consistently and across sectors if an automated system is being used in a way that impacts their rights,

opportunities, or access. This knowledge should provide confidence in how the public is being treated, and trust in the

validity and reasonable use of automated systems.

• A lawyer representing an older client with disabilities who had been cut off from Medicaid-funded home

health-care assistance couldn't determine why, especially since the decision went against historical access

practices. In a court hearing, the lawyer learned from a witness that the state in which the older client

lived had recently adopted a new algorithm to determine eligibility.

The lack of a timely explanation made it

harder to understand and contest the decision.

• A formal child welfare investigation is opened against a parent based on an algorithm and without the parent

ever being notified that data was being collected and used as part of an algorithmic child maltreatment

risk assessment.

The lack of notice or an explanation makes it harder for those performing child

maltreatment assessments to validate the risk assessment and denies parents knowledge that could help them

contest a decision.

NOTICE &

EXPLANATION

WHY THIS PRINCIPLE IS IMPORTANT

This section provides a brief summary of the problems which the principle seeks to address and protect

against, including illustrative examples.

•

A predictive policing system claimed to identify individuals at greatest risk to commit or become the victim of

gun violence (based on automated analysis of social ties to gang members, criminal histories, previous experi-

ences of gun violence, and other factors) and led to individuals being placed on a watch list with no

explanation or public transparency regarding how the system came to its conclusions.

Both police and

the public deserve to understand why and how such a system is making these determinations.

•

A system awarding benefits changed its criteria invisibly. Individuals were denied benefits due to data entry

errors and other system flaws. These flaws were only revealed when an explanation of the system

was demanded and produced.

The lack of an explanation made it harder for errors to be corrected in a

timely manner.

NOTICE &

EXPLANATION

WHAT SHOULD BE EXPECTED OF AUTOMATED SYSTEMS

The expectations for automated systems are meant to serve as a blueprint for the development of additional

technical standards and practices that are tailored for particular sectors and contexts.

An automated system should provide demonstrably clear, timely, understandable, and accessible notice of use, and

explanations as to how and why a decision was made or an action was taken by the system. These expectations are

explained below.

Provide clear, timely, understandable, and accessible notice of use and explanations

Generally accessible plain language documentation. The entity responsible for using the automated

system should ensure that documentation describing the overall system (including any human components) is

public and easy to find. The documentation should describe, in plain language, how the system works and how

any automated component is used to determine an action or decision. It should also include expectations about

reporting described throughout this framework, such as the algorithmic impact assessments described as

part of Algorithmic Discrimination Protections.

Accountable. Notices should clearly identify the entity responsible for designing each component of the

system and the entity using it.

Timely and up-to-date. Users should receive notice of the use of automated systems in advance of using or

while being impacted by the technology. An explanation should be available with the decision itself, or soon

thereafter. Notice should be kept up-to-date and people impacted by the system should be notified of use case

or key functionality changes.

Brief and clear. Notices and explanations should be assessed, such as by research on users’ experiences,

including user testing, to ensure that the people using or impacted by the automated system are able to easily

find notices and explanations, read them quickly, and understand and act on them. This includes ensuring that

notices and explanations are accessible to users with disabilities and are available in the language(s) and read-

ing level appropriate for the audience. Notices and explanations may need to be available in multiple forms,

(e.g., on paper, on a physical sign, or online), in order to meet these expectations and to be accessible to the

American public.

Provide explanations as to how and why a decision was made or an action was taken by an

automated system

Tailored to the purpose. Explanations should be tailored to the specific purpose for which the user is

expected to use the explanation, and should clearly state that purpose. An informational explanation might

differ from an explanation provided to allow for the possibility of recourse, an appeal, or one provided in the

context of a dispute or contestation process. For the purposes of this framework, 'explanation' should be

construed broadly. An explanation need not be a plain-language statement about causality but could consist of

any mechanism that allows the recipient to build the necessary understanding and intuitions to achieve the

stated purpose. Tailoring should be assessed (e.g., via user experience research).

Tailored to the target of the explanation. Explanations should be targeted to specific audiences and

clearly state that audience. An explanation provided to the subject of a decision might differ from one provided

to an advocate, or to a domain expert or decision maker. Tailoring should be assessed (e.g., via user experience

research).

NOTICE &

EXPLANATION

WHAT SHOULD BE EXPECTED OF AUTOMATED SYSTEMS

The expectations for automated systems are meant to serve as a blueprint for the development of additional

technical standards and practices that are tailored for particular sectors and contexts.

Tailored to the level of risk. An assessment should be done to determine the level of risk of the auto-

mated system. In settings where the consequences are high as determined by a risk assessment, or extensive

oversight is expected (e.g., in criminal justice or some public sector settings), explanatory mechanisms should

be built into the system design so that the system’s full behavior can be explained in advance (i.e., only fully

transparent models should be used), rather than as an after-the-decision interpretation. In other settings, the

extent of explanation provided should be tailored to the risk level.

Valid. The explanation provided by a system should accurately reflect the factors and the influences that led

to a particular decision, and should be meaningful for the particular customization based on purpose, target,

and level of risk. While approximation and simplification may be necessary for the system to succeed based on

the explanatory purpose and target of the explanation, or to account for the risk of fraud or other concerns

related to revealing decision-making information, such simplifications should be done in a scientifically

supportable way. Where appropriate based on the explanatory system, error ranges for the explanation should

be calculated and included in the explanation, with the choice of presentation of such information balanced

with usability and overall interface complexity concerns.

Demonstrate protections for notice and explanation

Reporting. Summary reporting should document the determinations made based on the above consider-

ations, including: the responsible entities for accountability purposes; the goal and use cases for the system,

identified users, and impacted populations; the assessment of notice clarity and timeliness; the assessment of

the explanation's validity and accessibility; the assessment of the level of risk; and the account and assessment

of how explanations are tailored, including to the purpose, the recipient of the explanation, and the level of

risk. Individualized profile information should be made readily available to the greatest extent possible that

includes explanations for any system impacts or inferences. Reporting should be provided in a clear plain

language and machine-readable manner.

NOTICE &

EXPLANATION

OW THESE PRINCIPLES CAN MOVE INTO PRACTICE

Real-life examples of how these principles can become reality, through laws, policies, and practical

technical and sociotechnical approaches to protecting rights, opportunities, and access.

People in Illinois are given written notice by the private sector if their biometric informa-

tion is used. The Biometric Information Privacy Act enacted by the state contains a number of provisions

concerning the use of individual biometric data and identifiers. Included among them is a provision that no private

entity may "collect, capture, purchase, receive through trade, or otherwise obtain" such information about an

individual, unless written notice is provided to that individual or their legally appointed representative.

Major technology companies are piloting new ways to communicate with the public about

their automated technologies. For example, a collection of non-profit organizations and companies have

worked together to develop a framework that defines operational approaches to transparency for machine

learning systems.

This framework, and others like it

,89

inform the public about the use of these tools, going

beyond simple notice to include reporting elements such as safety evaluations, disparity assessments, and

explanations of how the systems work.

Lenders are required by federal law to notify consumers about certain decisions made about

them. Both the Fair Credit Reporting Act and the Equal Credit Opportunity Act require in certain circumstances

that consumers who are denied credit receive "adverse action" notices. Anyone who relies on the information in a

credit report to deny a consumer credit must, under the Fair Credit Reporting Act, provide an "adverse action"

notice to the consumer, which includes "notice of the reasons a creditor took adverse action on the application

or on an existing credit account."

In addition, under the risk-based pricing rule,

lenders must either inform

borrowers of their credit score, or else tell consumers when "they are getting worse terms because of

information in their credit report." The CFPB has also asserted that "[t]he law gives every applicant the right to

a specific explanation if their application for credit was denied, and that right is not diminished simply because

a company uses a complex algorithm that it doesn't understand."

Such explanations illustrate a shared value

that certain decisions need to be explained.

A California law requires that warehouse employees are provided with notice and explana-

tion about quotas, potentially facilitated by automated systems, that apply to them. Warehous-

ing employers in California that use quota systems (often facilitated by algorithmic monitoring systems) are

required to provide employees with a written description of each quota that applies to the employee, including

“quantified number of tasks to be performed or materials to be produced or handled, within the defined

time period, and any potential adverse employment action that could result from failure to meet the quota.”

Across the federal government, agencies are conducting and supporting research on explain-

able AI systems. The NIST is conducting fundamental research on the explainability of AI systems. A multidis-

ciplinary team of researchers aims to develop measurement methods and best practices to support the

implementation of core tenets of explainable AI.

The Defense Advanced Research Projects Agency has a

program on Explainable Artificial Intelligence that aims to create a suite of machine learning techniques that

produce more explainable models, while maintaining a high level of learning performance (prediction

accuracy), and enable human users to understand, appropriately trust, and effectively manage the emerging

generation of artificially intelligent partners.

The National Science Foundation’s program on Fairness in

Artificial Intelligence also includes a specific interest in research foundations for explainable AI.

You should be able to opt out, where appropriate, and

have access to a person who can quickly consider and

remedy problems you encounter. You should be able to opt

out from automated systems in favor of a human alternative, where

appropriate. Appropriateness should be determined based on rea-

sonable expectations in a given context and with a focus on ensuring

broad accessibility and protecting the public from especially harm-

ful impacts. In some cases, a human or other alternative may be re-

quired by law. You should have access to timely human consider-

ation and remedy by a fallback and escalation process if an automat-

ed system fails, it produces an error, or you would like to appeal or

contest its impacts on you. Human consideration and fallback

should be accessible, equitable, effective, maintained, accompanied

by appropriate operator training, and should not impose an unrea-

sonable burden on the public. Automated systems with an intended

use within sensitive domains, including, but not limited to, criminal

justice, employment, education, and health, should additionally be

tailored to the purpose, provide meaningful access for oversight,

include training for any people interacting with the system, and in-

corporate human consideration for adverse or high-risk decisions.

Reporting that includes a description of these human governance

processes and assessment of their timeliness, accessibility, out-

comes, and effectiveness should be made public whenever possible.

HUMAN ALTERNATIVES, CONSIDERATION ALLBACKFAND,

HUMAN ALTERNATIVES,

CONSIDERATION, AND

FALLBACK

WHY THIS PRINCIPLE IS IMPORTANT

This section provides a brief summary of the problems which the principle seeks to address and protect

against, including illustrative examples.

There are many reasons people may prefer not to use an automated system: the system can be flawed and can lead to

unintended outcomes; it may reinforce bias or be inaccessible; it may simply be inconvenient or unavailable; or it may

replace a paper or manual process to which people had grown accustomed. Yet members of the public are often

presented with no alternative, or are forced to endure a cumbersome process to reach a human decision-maker once

they decide they no longer want to deal exclusively with the automated system or be impacted by its results. As a result

of this lack of human reconsideration, many receive delayed access, or lose access, to rights, opportunities, benefits,

and critical services. The American public deserves the assurance that, when rights, opportunities, or access are

meaningfully at stake and there is a reasonable expectation of an alternative to an automated system, they can conve-

niently opt out of an automated system and will not be disadvantaged for that choice. In some cases, such a human or

other alternative may be required by law, for example it could be required as “reasonable accommodations” for people

with disabilities.

In addition to being able to opt out and use a human alternative, the American public deserves a human fallback

system in the event that an automated system fails or causes harm. No matter how rigorously an automated system is

tested, there will always be situations for which the system fails. The American public deserves protection via human

review against these outlying or unexpected scenarios. In the case of time-critical systems, the public should not have

to wait—immediate human consideration and fallback should be available. In many time-critical systems, such a

remedy is already immediately available, such as a building manager who can open a door in the case an automated

card access system fails.

In the criminal justice system, employment, education, healthcare, and other sensitive domains, automated systems

are used for many purposes, from pre-trial risk assessments and parole decisions to technologies that help doctors

diagnose disease. Absent appropriate safeguards, these technologies can lead to unfair, inaccurate, or dangerous

outcomes. These sensitive domains require extra protections. It is critically important that there is extensive human

oversight in such settings.

These critical protections have been adopted in some scenarios. Where automated systems have been introduced to

provide the public access to government benefits, existing human paper and phone-based processes are generally still

in place, providing an important alternative to ensure access. Companies that have introduced automated call centers

often retain the option of dialing zero to reach an operator. When automated identity controls are in place to board an

airplane or enter the country, there is a person supervising the systems who can be turned to for help or to appeal a

misidentification.

The American people deserve the reassurance that such procedures are in place to protect their rights, opportunities,

and access. People make mistakes, and a human alternative or fallback mechanism will not always have the right

answer, but they serve as an important check on the power and validity of automated systems.

•

An automated signature matching system is used as part of the voting process in many parts of the country to

determine whether the signature on a mail-in ballot matches the signature on file. These signature matching

systems are less likely to work correctly for some voters, including voters with mental or physical

disabilities, voters with shorter or hyphenated names, and voters who have changed their name.

A human

curing process,

which helps voters to confirm their signatures and correct other voting mistakes, is

important to ensure all votes are counted,

and it is already standard practice in much of the country for

both an election official and the voter to have the opportunity to review and correct any such issues.

100

HUMAN ALTERNATIVES,

CONSIDERATION, AND

FALLBACK

WHY THIS PRINCIPLE IS IMPORTANT

This section provides a brief summary of the problems which the principle seeks to address and protect

against, including illustrative examples.

•

An unemployment benefits system in Colorado required, as a condition of accessing benefits, that applicants

have a smartphone in order to verify their identity. No alternative human option was readily available,

which denied many people access to benefits.

101

•

A fraud detection system for unemployment insurance distribution incorrectly flagged entries as fraudulent,

leading to people with slight discrepancies or complexities in their files having their wages withheld and tax

returns seized without any chance to explain themselves or receive a review by a person.

102

•

A patient was wrongly denied access to pain medication when the hospital’s software confused her medica-

tion history with that of her dog’s. Even after she tracked down an explanation for the problem, doctors

were afraid to override the system, and she was forced to go without pain relief due to the system’s error.

103

•

A large corporation automated performance evaluation and other HR functions, leading to workers being

fired by an automated system without the possibility of human review, appeal or other form of recourse.

104

HUMAN ALTERNATIVES,

CONSIDERATION, AND

FALLBACK

WHAT SHOULD BE EXPECTED OF AUTOMATED SYSTEMS

The expectations for automated systems are meant to serve as a blueprint for the development of additional

technical standards and practices that are tailored for particular sectors and contexts.

An automated system should provide demonstrably effective mechanisms to opt out in favor of a human alterna-

tive, where appropriate, as well as timely human consideration and remedy by a fallback system, with additional

human oversight and safeguards for systems used in sensitive domains, and with training and assessment for any

human-based portions of the system to ensure effectiveness.

Provide a mechanism to conveniently opt out from automated systems in favor of a human

alternative, where appropriate

Brief, clear, accessible notice and instructions. Those impacted by an automated system should be

given a brief, clear notice that they are entitled to opt-out, along with clear instructions for how to opt-out.

Instructions should be provided in an accessible form and should be easily findable by those impacted by the

automated system. The brevity, clarity, and accessibility of the notice and instructions should be assessed (e.g.,

via user experience research).

Human alternatives provided when appropriate. In many scenarios, there is a reasonable expectation

of human involvement in attaining rights, opportunities, or access. When automated systems make up part of

the attainment process, alternative timely human-driven processes should be provided. The use of a human

alternative should be triggered by an opt-out process.

Timely and not burdensome human alternative. Opting out should be timely and not unreasonably

burdensome in both the process of requesting to opt-out and the human-driven alternative provided.

Provide timely human consideration and remedy by a fallback and escalation system in the

event that an automated system fails, produces error, or you would like to appeal or con-

test its impacts on you

Proportionate. The availability of human consideration and fallback, along with associated training and

safeguards against human bias, should be proportionate to the potential of the automated system to meaning-

fully impact rights, opportunities, or access. Automated systems that have greater control over outcomes,

provide input to high-stakes decisions, relate to sensitive domains, or otherwise have a greater potential to

meaningfully impact rights, opportunities, or access should have greater availability (e.g., staffing) and over-

sight of human consideration and fallback mechanisms.

Accessible. Mechanisms for human consideration and fallback, whether in-person, on paper, by phone, or

otherwise provided, should be easy to find and use. These mechanisms should be tested to ensure that users

who have trouble with the automated system are able to use human consideration and fallback, with the under-

standing that it may be these users who are most likely to need the human assistance. Similarly, it should be

tested to ensure that users with disabilities are able to find and use human consideration and fallback and also

request reasonable accommodations or modifications.

Convenient. Mechanisms for human consideration and fallback should not be unreasonably burdensome as

compared to the automated system’s equivalent.

HUMAN ALTERNATIVES,

CONSIDERATION, AND

FALLBACK

WHAT SHOULD BE EXPECTED OF AUTOMATED SYSTEMS

The expectations for automated systems are meant to serve as a blueprint for the development of additional

technical standards and practices that are tailored for particular sectors and contexts.

Equitable. Consideration should be given to ensuring outcomes of the fallback and escalation system are

equitable when compared to those of the automated system and such that the fallback and escalation

system provides equitable access to underserved communities.

105

Timely. Human consideration and fallback are only useful if they are conducted and concluded in a

timely manner. The determination of what is timely should be made relative to the specific automated

system, and the review system should be staffed and regularly assessed to ensure it is providing timely

consideration and fallback. In time-critical systems, this mechanism should be immediately available or,

where possible, available before the harm occurs. Time-critical systems include, but are not limited to,

voting-related systems, automated building access and other access systems, systems that form a critical

component of healthcare, and systems that have the ability to withhold wages or otherwise cause

immediate financial penalties.

Effective. The organizational structure surrounding processes for consideration and fallback should

be designed so that if the human decision-maker charged with reassessing a decision determines that it

should be overruled, the new decision will be effectively enacted. This includes ensuring that the new

decision is entered into the automated system throughout its components, any previous repercussions from

the old decision are also overturned, and safeguards are put in place to help ensure that future decisions do

not result in the same errors.

Maintained. The human consideration and fallback process and any associated automated processes

should be maintained and supported as long as the relevant automated system continues to be in use.

Institute training, assessment, and oversight to combat automation bias and ensure any

human-based components of a system are effective.

Training and assessment. Anyone administering, interacting with, or interpreting the outputs of an auto-

mated system should receive training in that system, including how to properly interpret outputs of a system

in light of its intended purpose and in how to mitigate the effects of automation bias. The training should reoc-

cur regularly to ensure it is up to date with the system and to ensure the system is used appropriately. Assess-

ment should be ongoing to ensure that the use of the system with human involvement provides for appropri-

ate results, i.e., that the involvement of people does not invalidate the system's assessment as safe and effective

or lead to algorithmic discrimination.

Oversight. Human-based systems have the potential for bias, including automation bias, as well as other

concerns that may limit their effectiveness. The results of assessments of the efficacy and potential bias of

such human-based systems should be overseen by governance structures that have the potential to update the

operation of the human-based system in order to mitigate these effects.

HUMAN ALTERNATIVES,

CONSIDERATION, AND

FALLBACK

WHAT SHOULD BE EXPECTED OF AUTOMATED SYSTEMS

The expectations for automated systems are meant to serve as a blueprint for the development of additional

technical standards and practices that are tailored for particular sectors and contexts.

Implement additional human oversight and safeguards for automated systems related to

sensitive domains

Automated systems used within sensitive domains, including criminal justice, employment, education, and

health, should meet the expectations laid out throughout this framework, especially avoiding capricious,

inappropriate, and discriminatory impacts of these technologies. Additionally, automated systems used within

sensitive domains should meet these expectations:

Narrowly scoped data and inferences. Human oversight should ensure that automated systems in

sensitive domains are narrowly scoped to address a defined goal, justifying each included data item or attri-

bute as relevant to the specific use case. Data included should be carefully limited to avoid algorithmic

discrimination resulting from, e.g., use of community characteristics, social network analysis, or group-based

inferences.

Tailored to the situation. Human oversight should ensure that automated systems in sensitive domains

are tailored to the specific use case and real-world deployment scenario, and evaluation testing should show

that the system is safe and effective for that specific situation. Validation testing performed based on one loca-

tion or use case should not be assumed to transfer to another.

Human consideration before any high-risk decision. Automated systems, where they are used in

sensitive domains, may play a role in directly providing information or otherwise providing positive outcomes

to impacted people. However, automated systems should not be allowed to directly intervene in high-risk

situations, such as sentencing decisions or medical care, without human consideration.

Meaningful access to examine the system. Designers, developers, and deployers of automated

systems should consider limited waivers of confidentiality (including those related to trade secrets) where

necessary in order to provide meaningful oversight of systems used in sensitive domains, incorporating mea-

sures to protect intellectual property and trade secrets from unwarranted disclosure as appropriate. This

includes (potentially private and protected) meaningful access to source code, documentation, and related

data during any associated legal discovery, subject to effective confidentiality or court orders. Such meaning-

ful access should include (but is not limited to) adhering to the principle on Notice and Explanation using the

highest level of risk so the system is designed with built-in explanations; such systems should use fully-trans-

parent models where the model itself can be understood by people needing to directly examine it.

Demonstrate access to human alternatives, consideration, and fallback

Reporting. Reporting should include an assessment of timeliness and the extent of additional burden for

human alternatives, aggregate statistics about who chooses the human alternative, along with the results of

the assessment about brevity, clarity, and accessibility of notice and opt-out instructions. Reporting on the

accessibility, timeliness, and effectiveness of human consideration and fallback should be made public at regu-

lar intervals for as long as the system is in use. This should include aggregated information about the number

and type of requests for consideration, fallback employed, and any repeated requests; the timeliness of the

handling of these requests, including mean wait times for different types of requests as well as maximum wait

times; and information about the procedures used to address requests for consideration along with the results

of the evaluation of their accessibility. For systems used in sensitive domains, reporting should include infor-

mation about training and governance procedures for these technologies. Reporting should also include docu-

mentation of goals and assessment of meeting those goals, consideration of data included, and documentation

of the governance of reasonable access to the technology. Reporting should be provided in a clear and

machine-readable manner.

HUMAN ALTERNATIVES,

CONSIDERATION, AND

FALLBACK

HOW THESE PRINCIPLES CAN MOVE INTO PRACTICE

Real-life examples of how these principles can become reality, through laws, policies, and practical

technical and sociotechnical approaches to protecting rights, opportunities, and access.

Healthcare “navigators” help people find their way through online signup forms to choose

and obtain healthcare. A Navigator is “an individual or organization that's trained and able to help

consumers, small businesses, and their employees as they look for health coverage options through the

Marketplace (a government web site), including completing eligibility and enrollment forms.”

106

For

the 2022 plan year, the Biden-Harris Administration increased funding so that grantee organizations could

“train and certify more than 1,500 Navigators to help uninsured consumers find affordable and comprehensive

health coverage.”

107

The customer service industry has successfully integrated automated services such as

chat-bots and AI-driven call response systems with escalation to a human support

team.

108

Many businesses now use partially automated customer service platforms that help answer customer

questions and compile common problems for human agents to review. These integrated human-AI

systems allow companies to provide faster customer care while maintaining human agents to answer

calls or otherwise respond to complicated requests. Using both AI and human agents is viewed as key to

successful customer service.

109

Ballot curing laws in at least 24 states require a fallback system that allows voters to

correct their ballot and have it counted in the case that a voter signature matching

algorithm incorrectly flags their ballot as invalid or there is another issue with their

ballot, and review by an election official does not rectify the problem. Some federal

courts have found that such cure procedures are constitutionally required.

110

Ballot

curing processes vary among states, and include direct phone calls, emails, or mail contact by election

officials.

111

Voters are asked to provide alternative information or a new signature to verify the validity of their

ballot.

APPENDIX

Examples of Automated Systems

The below examples are meant to illustrate the breadth of automated systems that, insofar as they have the

potential to meaningfully impact rights, opportunities, or access to critical resources or services, should

be covered by the Blueprint for an AI Bill of Rights. These examples should not be construed to limit that

scope, which includes automated systems that may not yet exist, but which fall under these criteria.

Examples of automated systems for which the Blueprint for an AI Bill of Rights should be considered include

those that have the potential to meaningfully impact:

• Civil rights, civil liberties, or privacy, including but not limited to:

Speech-related systems such as automated content moderation tools;

Surveillance and criminal justice system algorithms such as risk assessments, predictive

policing, automated license plate readers, real-time facial recognition systems (especially

those used in public places or during protected activities like peaceful protests), social media

monitoring, and ankle monitoring devices;

Voting-related systems such as signature matching tools;

Systems with a potential privacy impact such as smart home systems and associated data,

systems that use or collect health-related data, systems that use or collect education-related

data, criminal justice system data, ad-targeting systems, and systems that perform big data

analytics in order to build profiles or infer personal information about individuals; and

Any system that has the meaningful potential to lead to algorithmic discrimination.

• Equal opportunities, including but not limited to:

Education-related systems such as algorithms that purport to detect student cheating or

plagiarism, admissions algorithms, online or virtual reality student monitoring systems,

projections of student progress or outcomes, algorithms that determine access to resources or

rograms, and surveillance of classes (whether online or in-person);

Housing-related systems such as tenant screening algorithms, automated valuation systems that

estimate the value of homes used in mortgage underwriting or home insurance, and automated

valuations from online aggregator websites; and

Employment-related systems such as workplace algorithms that inform all aspects of the terms

and conditions of employment including, but not limited to, pay or promotion, hiring or termina-

tion algorithms, virtual or augmented reality workplace training programs, and electronic work

place surveillance and management systems.

• Access to critical resources and services, including but not limited to:

Health and health insurance technologies such as medical AI systems and devices, AI-assisted

diagnostic tools, algorithms or predictive models used to support clinical decision making, medical

or insurance health risk assessments, drug addiction risk assessments and associated access alg

-orithms, wearable technologies, wellness apps, insurance care allocation algorithms, and health

insurance cost and underwriting algorithms;

Financial system algorithms such as loan allocation algorithms, financial system access determi-

nation algorithms, credit scoring systems, insurance algorithms including risk assessments, auto

-mated interest rate determinations, and financial algorithms that apply penalties (e.g., that can

garnish wages or withhold tax returns);

APPENDIX

Systems that impact the safety of communities such as automated traffic control systems, elec

-ctrical grid controls, smart city technologies, and industrial emissions and environmental

impact control algorithms; and

Systems related to access to benefits or services or assignment of penalties such as systems that

support decision-makers who adjudicate benefits such as collating or analyzing information or

matching records, systems which similarly assist in the adjudication of administrative or criminal

penalties, fraud detection algorithms, services or benefits access control algorithms, biometric

systems used as access control, and systems which make benefits or services related decisions on a

fully or partially autonomous basis (such as a determination to revoke benefits).

ECTION TITLE

APPENDIX

Listening to the American People

The White House Office of Science and Technology Policy (OSTP) led a yearlong process to seek and distill

input from people across the country – from impacted communities to industry stakeholders to

technology developers to other experts across fields and sectors, as well as policymakers across the Federal

government – on the issue of algorithmic and data-driven harms and potential remedies. Through panel

discussions, public listening sessions, private meetings, a formal request for information, and input to a

publicly accessible and widely-publicized email address, people across the United States spoke up about

both the promises and potential harms of these technologies, and played a central role in shaping the

Blueprint for an AI Bill of Rights.

Panel Discussions to Inform the Blueprint for An AI Bill of Rights

OSTP co-hosted a series of six panel discussions in collaboration with the Center for American Progress,

the Joint Center for Political and Economic Studies, New America, the German Marshall Fund, the Electronic

Privacy Information Center, and the Mozilla Foundation. The purpose of these convenings – recordings of

which are publicly available online

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– was to bring together a variety of experts, practitioners, advocates

and federal government officials to offer insights and analysis on the risks, harms, benefits, and

policy opportunities of automated systems. Each panel discussion was organized around a wide-ranging

theme, exploring current challenges and concerns and considering what an automated society that

respects democratic values should look like. These discussions focused on the topics of consumer

rights and protections, the criminal justice system, equal opportunities and civil justice, artificial

intelligence and democratic values, social welfare and development, and the healthcare system.

Summaries of Panel Discussions:

Panel 1: Consumer Rights and Protections. This event explored the opportunities and challenges for

individual consumers and communities in the context of a growing ecosystem of AI-enabled consumer

products, advanced platforms and services, “Internet of Things” (IoT) devices, and smart city products and

services.

Welcome:

• Rashida Richardson, Senior Policy Advisor for Data and Democracy, White House Office of Science and

Technology Policy

• Karen Kornbluh, Senior Fellow and Director of the Digital Innovation and Democracy Initiative, German

Marshall Fund

Moderator:

Devin E. Willis, Attorney, Division of Privacy and Identity Protection, Bureau of Consumer Protection, Federal

Trade Commission

Panelists:

• Tamika L. Butler, Principal, Tamika L. Butler Consulting

• Jennifer Clark, Professor and Head of City and Regional Planning, Knowlton School of Engineering, Ohio

State University

• Carl Holshouser, Senior Vice President for Operations and Strategic Initiatives, TechNet

• Surya Mattu, Senior Data Engineer and Investigative Data Journalist, The Markup

• Mariah Montgomery, National Campaign Director, Partnership for Working Families

APPENDIX

Panelists discussed the benefits of AI-enabled systems and their potential to build better and more

innovative infrastructure. They individually noted that while AI technologies may be new, the process of

technological diffusion is not, and that it was critical to have thoughtful and responsible development and

integration of technology within communities. Some panelists suggested that the integration of technology

could benefit from examining how technological diffusion has worked in the realm of urban planning:

lessons learned from successes and failures there include the importance of balancing ownership rights, use

rights, and community health, safety and welfare, as well ensuring better representation of all voices,

especially those traditionally marginalized by technological advances. Some panelists also raised the issue of

power structures – providing examples of how strong transparency requirements in smart city projects

helped to reshape power and give more voice to those lacking the financial or political power to effect change.

In discussion of technical and governance interventions that that are needed to protect against the harms

of these technologies, various panelists emphasized the need for transparency, data collection, and

flexible and reactive policy development, analogous to how software is continuously updated and deployed.

Some panelists pointed out that companies need clear guidelines to have a consistent environment for

innovation, with principles and guardrails being the key to fostering responsible innovation.

Panel 2: The Criminal Justice System. This event explored current and emergent uses of technology in

the criminal justice system and considered how they advance or undermine public safety, justice, and

democratic values.

Welcome:

• Suresh Venkatasubramanian, Assistant Director for Science and Justice, White House Office of Science

and Technology Policy

• Ben Winters, Counsel, Electronic Privacy Information Center

Moderator: Chiraag Bains, Deputy Assistant to the President on Racial Justice & Equity

Panelists:

• Sean Malinowski, Director of Policing Innovation and Reform, University of Chicago Crime Lab

• Kristian Lum, Researcher

• Jumana Musa, Director, Fourth Amendment Center, National Association of Criminal Defense Lawyers

• Stanley Andrisse, Executive Director, From Prison Cells to PHD; Assistant Professor, Howard University

College of Medicine

• Myaisha Hayes, Campaign Strategies Director, MediaJustice

Panelists discussed uses of technology within the criminal justice system, including the use of predictive

policing, pretrial risk assessments, automated license plate readers, and prison communication tools. The

discussion emphasized that communities deserve safety, and strategies need to be identified that lead to safety;

such strategies might include data-driven approaches, but the focus on safety should be primary, and

technology may or may not be part of an effective set of mechanisms to achieve safety. Various panelists raised

concerns about the validity of these systems, the tendency of adverse or irrelevant data to lead to a replication of

unjust outcomes, and the confirmation bias and tendency of people to defer to potentially inaccurate automated

systems. Throughout, many of the panelists individually emphasized that the impact of these systems on

individuals and communities is potentially severe: the systems lack individualization and work against the

belief that people can change for the better, system use can lead to the loss of jobs and custody of children, and

surveillance can lead to chilling effects for communities and sends negative signals to community members

about how they're viewed.

In discussion of technical and governance interventions that that are needed to protect against the harms of

these technologies, various panelists emphasized that transparency is important but is not enough to achieve

accountability. Some panelists discussed their individual views on additional system needs for validity, and

agreed upon the importance of advisory boards and compensated community input early in the design process

(before the technology is built and instituted). Various panelists also emphasized the importance of regulation

that includes limits to the type and cost of such technologies.

APPENDIX

Panel 3: Equal Opportunities and Civil Justice. This event explored current and emerging uses of

technology that impact equity of opportunity in employment, education, and housing.

Welcome:

• Rashida Richardson, Senior Policy Advisor for Data and Democracy, White House Office of Science and

Technology Policy

• Dominique Harrison, Director for Technology Policy, The Joint Center for Political and Economic

Studies

Moderator: Jenny Yang, Director, Office of Federal Contract Compliance Programs, Department of Labor

Panelists:

• Christo Wilson, Associate Professor of Computer Science, Northeastern University

• Frida Polli, CEO, Pymetrics

• Karen Levy, Assistant Professor, Department of Information Science, Cornell University

• Natasha Duarte, Project Director, Upturn

• Elana Zeide, Assistant Professor, University of Nebraska College of Law

• Fabian Rogers, Constituent Advocate, Office of NY State Senator Jabari Brisport and Community

Advocate and Floor Captain, Atlantic Plaza Towers Tenants Association

The individual panelists described the ways in which AI systems and other technologies are increasingly being

used to limit access to equal opportunities in education, housing, and employment. Education-related

concerning uses included the increased use of remote proctoring systems, student location and facial

recognition tracking, teacher evaluation systems, robot teachers, and more. Housing-related concerning uses

including automated tenant background screening and facial recognition-based controls to enter or exit

housing complexes. Employment-related concerning uses included discrimination in automated hiring

screening and workplace surveillance. Various panelists raised the limitations of existing privacy law as a key

concern, pointing out that students should be able to reinvent themselves and require privacy of their student

records and education-related data in order to do so. The overarching concerns of surveillance in these

domains included concerns about the chilling effects of surveillance on student expression, inappropriate

control of tenants via surveillance, and the way that surveillance of workers blurs the boundary between work

and life and exerts extreme and potentially damaging control over workers' lives. Additionally, some panelists

pointed out ways that data from one situation was misapplied in another in a way that limited people's

opportunities, for example data from criminal justice settings or previous evictions being used to block further

access to housing. Throughout, various panelists emphasized that these technologies are being used to shift the

burden of oversight and efficiency from employers to workers, schools to students, and landlords to tenants, in

ways that diminish and encroach on equality of opportunity; assessment of these technologies should include

whether they are genuinely helpful in solving an identified problem.

In discussion of technical and governance interventions that that are needed to protect against the harms of

these technologies, panelists individually described the importance of: receiving community input into the

design and use of technologies, public reporting on crucial elements of these systems, better notice and consent

procedures that ensure privacy based on context and use case, ability to opt-out of using these systems and

receive a fallback to a human process, providing explanations of decisions and how these systems work, the

need for governance including training in using these systems, ensuring the technological use cases are

genuinely related to the goal task and are locally validated to work, and the need for institution and protection

of third party audits to ensure systems continue to be accountable and valid.

APPENDIX

Panel 4: Artificial Intelligence and Democratic Values. This event examined challenges and opportunities in

the design of technology that can help support a democratic vision for AI. It included discussion of the

technical aspects of designing non-discriminatory technology, explainable AI, human-computer

interaction with an emphasis on community participation, and privacy-aware design.

Welcome:

• Sorelle Friedler, Assistant Director for Data and Democracy, White House Office of Science and

Technology Policy

• J. Bob Alotta, Vice President for Global Programs, Mozilla Foundation

• Navrina Singh, Board Member, Mozilla Foundation

Moderator: Kathy Pham Evans, Deputy Chief Technology Officer for Product and Engineering, U.S

Federal Trade Commission.

Panelists:

• Liz O’Sullivan, CEO, Parity AI

• Timnit Gebru, Independent Scholar

• Jennifer Wortman Vaughan, Senior Principal Researcher, Microsoft Research, New York City

• Pamela Wisniewski, Associate Professor of Computer Science, University of Central Florida; Director,

Socio-technical Interaction Research (STIR) Lab

• Seny Kamara, Associate Professor of Computer Science, Brown University

Each panelist individually emphasized the risks of using AI in high-stakes settings, including the potential for

biased data and discriminatory outcomes, opaque decision-making processes, and lack of public trust and

understanding of the algorithmic systems. The interventions and key needs various panelists put forward as

necessary to the future design of critical AI systems included ongoing transparency, value sensitive and

participatory design, explanations designed for relevant stakeholders, and public consultation. Various

panelists emphasized the importance of placing trust in people, not technologies, and in engaging with

impacted communities to understand the potential harms of technologies and build protection by design into

future systems.

Panel 5: Social Welfare and Development. This event explored current and emerging uses of technology to

implement or improve social welfare systems, social development programs, and other systems that can impact

life chances.

Welcome:

• Suresh Venkatasubramanian, Assistant Director for Science and Justice, White House Office of Science

and Technology Policy

• Anne-Marie Slaughter, CEO, New America

Moderator: Michele Evermore, Deputy Director for Policy, Office of Unemployment Insurance

Modernization, Office of the Secretary, Department of Labor

Panelists:

• Blake Hall, CEO and Founder, ID.Me

• Karrie Karahalios, Professor of Computer Science, University of Illinois, Urbana-Champaign

• Christiaan van Veen, Director of Digital Welfare State and Human Rights Project, NYU School of Law's

Center for Human Rights and Global Justice

APPENDIX

• Julia Simon-Mishel, Supervising Attorney, Philadelphia Legal Assistance

• Dr. Zachary Mahafza, Research & Data Analyst, Southern Poverty Law Center

• J. Khadijah Abdurahman, Tech Impact Network Research Fellow, AI Now Institute, UCLA C2I1, and

UWA Law School

Panelists separately described the increasing scope of technology use in providing for social welfare, including

in fraud detection, digital ID systems, and other methods focused on improving efficiency and reducing cost.

However, various panelists individually cautioned that these systems may reduce burden for government

agencies by increasing the burden and agency of people using and interacting with these technologies.

Additionally, these systems can produce feedback loops and compounded harm, collecting data from

communities and using it to reinforce inequality. Various panelists suggested that these harms could be

mitigated by ensuring community input at the beginning of the design process, providing ways to opt out of

these systems and use associated human-driven mechanisms instead, ensuring timeliness of benefit payments,

and providing clear notice about the use of these systems and clear explanations of how and what the

technologies are doing. Some panelists suggested that technology should be used to help people receive

benefits, e.g., by pushing benefits to those in need and ensuring automated decision-making systems are only

used to provide a positive outcome; technology shouldn't be used to take supports away from people who need

them.

Panel 6: The Healthcare System. This event explored current and emerging uses of technology in the

healthcare system and consumer products related to health.

Welcome:

• Alondra Nelson, Deputy Director for Science and Society, White House Office of Science and Technology

Policy

• Patrick Gaspard, President and CEO, Center for American Progress

Moderator: Micky Tripathi, National Coordinator for Health Information Technology, U.S Department of

Health and Human Services.

Panelists:

• Mark Schneider, Health Innovation Advisor, ChristianaCare

• Ziad Obermeyer, Blue Cross of California Distinguished Associate Professor of Policy and Management,

University of California, Berkeley School of Public Health

• Dorothy Roberts, George A. Weiss University Professor of Law and Sociology and the Raymond Pace and

Sadie Tanner Mossell Alexander Professor of Civil Rights, University of Pennsylvania

• David Jones, A. Bernard Ackerman Professor of the Culture of Medicine, Harvard University

• Jamila Michener, Associate Professor of Government, Cornell University; Co-Director, Cornell Center for

Health Equity

Panelists discussed the impact of new technologies on health disparities; healthcare access, delivery, and

outcomes; and areas ripe for research and policymaking. Panelists discussed the increasing importance of tech-

nology as both a vehicle to deliver healthcare and a tool to enhance the quality of care. On the issue of

delivery, various panelists pointed to a number of concerns including access to and expense of broadband

service, the privacy concerns associated with telehealth systems, the expense associated with health

monitoring devices, and how this can exacerbate equity issues. On the issue of technology enhanced care,

some panelists spoke extensively about the way in which racial biases and the use of race in medicine

perpetuate harms and embed prior discrimination, and the importance of ensuring that the technologies used

in medical care were accountable to the relevant stakeholders. Various panelists emphasized the importance

having the voices of those subjected to these technologies be heard.

APPENDIX

Summaries of Additional Engagements:

• OSTP created an email address (ai-equity@ostp.eop.gov) to solicit comments from the public on the use of

artificial intelligence and other data-driven technologies in their lives.

• OSTP issued a Request For Information (RFI) on the use and governance of biometric technologies.

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The

purpose of this RFI was to understand the extent and variety of biometric technologies in past, current, or

planned use; the domains in which these technologies are being used; the entities making use of them; current

principles, practices, or policies governing their use; and the stakeholders that are, or may be, impacted by their

use or regulation. The 130 responses to this RFI are available in full online

114

and were submitted by the below

listed organizations and individuals:

Accenture

Access Now

ACT | The App Association

AHIP

AIethicist.org

Airlines for America

Alliance for Automotive Innovation

Amelia Winger-Bearskin

American Civil Liberties Union

American Civil Liberties Union of

Massachusetts

American Medical Association

ARTICLE19

Attorneys General of the District of

Columbia, Illinois, Maryland,

Michigan, Minnesota, New York,

North Carolina, Oregon, Vermont,

and Washington

Avanade

Aware

Barbara Evans

Better Identity Coalition

Bipartisan Policy Center

Brandon L. Garrett and Cynthia

Rudin

Brian Krupp

Brooklyn Defender Services

BSA | The Software Alliance

Carnegie Mellon University

Center for Democracy &

Technology

Center for New Democratic

Processes

Center for Research and Education

on Accessible Technology and

Experiences at University of

Washington, Devva Kasnitz, L Jean

Camp, Jonathan Lazar, Harry

Hochheiser

Center on Privacy & Technology at

Georgetown Law

Cisco Systems

City of Portland Smart City PDX

Program

CLEAR

Clearview AI

Cognoa

Color of Change

Common Sense Media

Computing Community Consortium

at Computing Research Association

Connected Health Initiative

Consumer Technology Association

Courtney Radsch

Coworker

Cyber Farm Labs

Data & Society Research Institute

Data for Black Lives

Data to Actionable Knowledge Lab

at Harvard University

Deloitte

Dev Technology Group

Digital Therapeutics Alliance

Digital Welfare State & Human

Rights Project and Center for

Human Rights and Global Justice at

New York University School of

Law, and Temple University

Institute for Law, Innovation &

Technology

Dignari

Douglas Goddard

Edgar Dworsky

Electronic Frontier Foundation

Electronic Privacy Information

Center, Center for Digital

Democracy, and Consumer

Federation of America

FaceTec

Fight for the Future

Ganesh Mani

Georgia Tech Research Institute

Google

Health Information Technology

Research and Development

Interagency Working Group

HireVue

HR Policy Association

ID.me

Identity and Data Sciences

Laboratory at Science Applications

International Corporation

Information Technology and

Innovation Foundation

Information Technology Industry

Council

Innocence Project

Institute for Human-Centered

Artificial Intelligence at Stanford

University

Integrated Justice Information

Systems Institute

International Association of Chiefs

of Police

International Biometrics + Identity

Association

International Business Machines

Corporation

International Committee of the Red

Cross

Inventionphysics

iProov

Jacob Boudreau

Jennifer K. Wagner, Dan Berger,

Margaret Hu, and Sara Katsanis

Jonathan Barry-Blocker

Joseph Turow

Joy Buolamwini

Joy Mack

Karen Bureau

Lamont Gholston

Lawyers’ Committee for Civil

Rights Under Law

APPENDIX

Lisa Feldman Barrett

Madeline Owens

Marsha Tudor

Microsoft Corporation

MITRE Corporation

National Association for the

Advancement of Colored People

Legal Defense and Educational

Fund

National Association of Criminal

Defense Lawyers

National Center for Missing &

Exploited Children

National Fair Housing Alliance

National Immigration Law Center

NEC Corporation of America

New America’s Open Technology

Institute

New York Civil Liberties Union

No Name Provided

Notre Dame Technology Ethics

Center

Office of the Ohio Public Defender

Onfido

Oosto

Orissa Rose

Palantir

Pangiam

Parity Technologies

Patrick A. Stewart, Jeffrey K.

Mullins, and Thomas J. Greitens

Pel Abbott

Philadelphia Unemployment

Project

Project On Government Oversight

Recording Industry Association of

America

Robert Wilkens

Ron Hedges

Science, Technology, and Public

Policy Program at University of

Michigan Ann Arbor

Security Industry Association

Sheila Dean

Software & Information Industry

Association

Stephanie Dinkins and the Future

Histories Studio at Stony Brook

University

TechNet

The Alliance for Media Arts and

Culture, MIT Open Documentary

Lab and Co-Creation Studio, and

Immerse

The International Brotherhood of

Teamsters

The Leadership Conference on

Civil and Human Rights

Thorn

U.S. Chamber of Commerce’s

Technology Engagement Center

Uber Technologies

University of Pittsburgh

Undergraduate Student

Collaborative

Upturn

US Technology Policy Committee

of the Association of Computing

Machinery

Virginia Puccio

Visar Berisha and Julie Liss

XR Association

XR Safety Initiative

• As an additional effort to reach out to stakeholders regarding the RFI, OSTP conducted two listening sessions

for members of the public. The listening sessions together drew upwards of 300 participants. The Science and

Technology Policy Institute produced a synopsis of both the RFI submissions and the feedback at the listening

sessions.

115

APPENDIX

• OSTP conducted meetings with a variety of stakeholders in the private sector and civil society. Some of these

meetings were specifically focused on providing ideas related to the development of the Blueprint for an AI

Bill of Rights while others provided useful general context on the positive use cases, potential harms, and/or

oversight possibilities for these technologies. Participants in these conversations from the private sector and

civil society included:

Adobe

American Civil Liberties Union

(ACLU)

The Aspen Commission on

Information Disorder

The Awood Center

The Australian Human Rights

Commission

Biometrics Institute

The Brookings Institute

BSA | The Software Alliance

Cantellus Group

Center for American Progress

Center for Democracy and

Technology

Center on Privacy and Technology

at Georgetown Law

Christiana Care

Color of Change

Coworker

Data Robot

Data Trust Alliance

Data and Society Research Institute

Deepmind

EdSAFE AI Alliance

Electronic Privacy Information

Center (EPIC)

Encode Justice

Equal AI

Google

Hitachi's AI Policy Committee

The Innocence Project

Institute of Electrical and

Electronics Engineers (IEEE)

Intuit

Lawyers Committee for Civil Rights

Under Law

Legal Aid Society

The Leadership Conference on

Civil and Human Rights